import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Import 2019 dataset
data_2019 = pd.read_csv("Jumlah Perjalanan Wisatawan Nusantara Menurut Provinsi Tujuan, 2019.csv", header=None)
print("Total instance and features:", data_2019.shape)
print("List of features:", data_2019.columns)
print("Print the first 5 instance:\n", data_2019.head())

Total instance and features: (43, 14)
List of features: Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], dtype='int64')
Print the first 5 instance:
             0                                                  1         2   \
0  38 Provinsi                                                NaN       NaN   
1          NaN  Jumlah Perjalanan Wisatawan Nusantara Menurut ...       NaN   
2          NaN                                               2019       NaN   
3          NaN                                            Januari  Februari   
4         ACEH                                            1387574   1111607   

        3        4        5        6        7        8          9        10  \
0      NaN      NaN      NaN      NaN      NaN      NaN        NaN      NaN   
1      NaN      NaN      NaN      NaN      NaN      NaN        NaN      NaN   
2      NaN      NaN      NaN      NaN      NaN      NaN        NaN      NaN   
3    Maret    April      Mei     Juni     Juli  Agustus  September  Oktober   
4  1113176  1155705  1070909  1800152  1323272  1508073    1174513  1219323   

         11        12        13  
0       NaN       NaN       NaN  
1       NaN       NaN       NaN  
2       NaN       NaN       NaN  
3  November  Desember   Tahunan  
4   1198958   1597524  15660786

# Import 2020 dataset
data_2020 = pd.read_csv("Jumlah Perjalanan Wisatawan Nusantara Menurut Provinsi Tujuan, 2020.csv", header=None)
print("Total instance and features:", data_2020.shape)
print("List of features:", data_2020.columns)
print("Print the first 5 instance:\n", data_2020.head())

Total instance and features: (43, 14)
List of features: Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], dtype='int64')
Print the first 5 instance:
             0                                                  1         2   \
0  38 Provinsi                                                NaN       NaN   
1          NaN  Jumlah Perjalanan Wisatawan Nusantara Menurut ...       NaN   
2          NaN                                               2020       NaN   
3          NaN                                            Januari  Februari   
4         ACEH                                             483897    430589   

       3       4       5       6       7        8          9        10  \
0     NaN     NaN     NaN     NaN     NaN      NaN        NaN      NaN   
1     NaN     NaN     NaN     NaN     NaN      NaN        NaN      NaN   
2     NaN     NaN     NaN     NaN     NaN      NaN        NaN      NaN   
3   Maret   April     Mei    Juni    Juli  Agustus  September  Oktober   
4  351710  157764  258310  298482  333571   392055     293602   340415   

         11        12       13  
0       NaN       NaN      NaN  
1       NaN       NaN      NaN  
2       NaN       NaN      NaN  
3  November  Desember  Tahunan  
4    322558    479226  4142179

# Import 2021 dataset
data_2021 = pd.read_csv("Jumlah Perjalanan Wisatawan Nusantara Menurut Provinsi Tujuan, 2021.csv", header=None)
print("Total instance and features:", data_2021.shape)
print("List of features:", data_2021.columns)
print("Print the first 5 instance:\n", data_2021.head())

Total instance and features: (43, 14)
List of features: Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], dtype='int64')
Print the first 5 instance:
             0                                                  1         2   \
0  38 Provinsi                                                NaN       NaN   
1          NaN  Jumlah Perjalanan Wisatawan Nusantara Menurut ...       NaN   
2          NaN                                               2021       NaN   
3          NaN                                            Januari  Februari   
4         ACEH                                             396953    421243   

       3       4       5       6       7        8          9        10  \
0     NaN     NaN     NaN     NaN     NaN      NaN        NaN      NaN   
1     NaN     NaN     NaN     NaN     NaN      NaN        NaN      NaN   
2     NaN     NaN     NaN     NaN     NaN      NaN        NaN      NaN   
3   Maret   April     Mei    Juni    Juli  Agustus  September  Oktober   
4  445804  378402  599117  457822  445355   397120     440652   490983   

         11        12       13  
0       NaN       NaN      NaN  
1       NaN       NaN      NaN  
2       NaN       NaN      NaN  
3  November  Desember  Tahunan  
4    445046    615908  5534405

# Import 2022 dataset
data_2022 = pd.read_csv("Jumlah Perjalanan Wisatawan Nusantara Menurut Provinsi Tujuan, 2022.csv", header=None)
print("Total instance and features:", data_2022.shape)
print("List of features:", data_2022.columns)
print("Print the first 5 instance:\n", data_2022.head())

Total instance and features: (43, 14)
List of features: Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], dtype='int64')
Print the first 5 instance:
             0                                                  1         2   \
0  38 Provinsi                                                NaN       NaN   
1          NaN  Jumlah Perjalanan Wisatawan Nusantara Menurut ...       NaN   
2          NaN                                               2022       NaN   
3          NaN                                            Januari  Februari   
4         ACEH                                             526417    501651   

       3       4       5       6       7        8          9        10  \
0     NaN     NaN     NaN     NaN     NaN      NaN        NaN      NaN   
1     NaN     NaN     NaN     NaN     NaN      NaN        NaN      NaN   
2     NaN     NaN     NaN     NaN     NaN      NaN        NaN      NaN   
3   Maret   April     Mei    Juni    Juli  Agustus  September  Oktober   
4  585675  434633  935025  566361  748573   531072     500673   513056   

         11        12       13  
0       NaN       NaN      NaN  
1       NaN       NaN      NaN  
2       NaN       NaN      NaN  
3  November  Desember  Tahunan  
4    471520    639922  6954578

# Import 2023 dataset
data_2023 = pd.read_csv("Jumlah Perjalanan Wisatawan Nusantara Menurut Provinsi Tujuan, 2023.csv", header=None)
print("Total instance and features:", data_2023.shape)
print("List of features:", data_2023.columns)
print("Print the first 5 instance:\n", data_2023.head())

Total instance and features: (43, 14)
List of features: Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], dtype='int64')
Print the first 5 instance:
             0                                                  1         2   \
0  38 Provinsi                                                NaN       NaN   
1          NaN  Jumlah Perjalanan Wisatawan Nusantara Menurut ...       NaN   
2          NaN                                               2023       NaN   
3          NaN                                            Januari  Februari   
4         ACEH                                             663437    517850   

       3       4       5       6       7        8          9        10  \
0     NaN     NaN     NaN     NaN     NaN      NaN        NaN      NaN   
1     NaN     NaN     NaN     NaN     NaN      NaN        NaN      NaN   
2     NaN     NaN     NaN     NaN     NaN      NaN        NaN      NaN   
3   Maret   April     Mei    Juni    Juli  Agustus  September  Oktober   
4  557272  756915  791968  701963  871519   618671     565184   588020   

         11        12       13  
0       NaN       NaN      NaN  
1       NaN       NaN      NaN  
2       NaN       NaN      NaN  
3  November  Desember  Tahunan  
4    637519   1673468  8943786

# Import 2024 dataset
data_2024 = pd.read_csv("Jumlah Perjalanan Wisatawan Nusantara Menurut Provinsi Tujuan, 2024.csv", header=None)
print("Total instance and features:", data_2024.shape)
print("List of features:", data_2024.columns)
print("Print the first 5 instance:\n", data_2024.head())

Total instance and features: (43, 14)
List of features: Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], dtype='int64')
Print the first 5 instance:
             0                                                  1         2   \
0  38 Provinsi                                                NaN       NaN   
1          NaN  Jumlah Perjalanan Wisatawan Nusantara Menurut ...       NaN   
2          NaN                                               2024       NaN   
3          NaN                                            Januari  Februari   
4         ACEH                                            1009785    937728   

       3        4       5        6       7        8          9        10  \
0     NaN      NaN     NaN      NaN     NaN      NaN        NaN      NaN   
1     NaN      NaN     NaN      NaN     NaN      NaN        NaN      NaN   
2     NaN      NaN     NaN      NaN     NaN      NaN        NaN      NaN   
3   Maret    April     Mei     Juni    Juli  Agustus  September  Oktober   
4  905565  1282532  995794  1065780  958675   918712    1121450        -   

         11        12       13  
0       NaN       NaN      NaN  
1       NaN       NaN      NaN  
2       NaN       NaN      NaN  
3  November  Desember  Tahunan  
4         -         -        -

# Drop 'Tahunan' features for 2019 dataset
data_2019 = data_2019.drop(columns=[13]) 

# Drop 'Provinsi' and 'Tahunan' features for 2020-2024 dataset
data_2020 = data_2020.drop(columns=[0, 13])  
data_2021 = data_2021.drop(columns=[0, 13])
data_2022 = data_2022.drop(columns=[0, 13])
data_2023 = data_2023.drop(columns=[0, 13]) 
data_2024 = data_2024.drop(columns=[0, 7, 8, 9, 10, 11, 12, 13]) 

# Print total of features after drop
print("Total features after drop (2019):", data_2019.shape[1])
print("Total features after drop (2020):", data_2020.shape[1])
print("Total features after drop (2021):", data_2021.shape[1])
print("Total features after drop (2022):", data_2022.shape[1])
print("Total features after drop (2023):", data_2023.shape[1])
print("Total features after drop (2024):", data_2024.shape[1])

Total features after drop (2019): 13
Total features after drop (2020): 12
Total features after drop (2021): 12
Total features after drop (2022): 12
Total features after drop (2023): 12
Total features after drop (2024): 6

# Combine the datasets using concat along features (axis=1)
data = pd.concat([data_2019, data_2020, data_2021, data_2022, data_2023, data_2024], axis=1)

# Drop instance 0, 1, and 2 which contain the titles and descriptions of the dataset
data = data.drop([0, 1, 2])

# Reset index
data.reset_index(drop=True, inplace=True)

# Transpose the dataset
data = data.T

data

# Create new feature names for the concatenated dataset
months = ['january', 'february', 'march', 'april', 'may', 'june', 'july', 'august', 'september', 'october', 'november', 'december']
years = ['2019', '2020', '2021', '2022', '2023']
months_2024 = ['january', 'february', 'march', 'april', 'may', 'june']

# Rename features to ['{month}_{year}' for each month and year]
index = [f'{month}_{year}' for year in years for month in months] + [f'{month}_2024' for month in months_2024]

# Drop the old index
data = data.drop(columns=[0])

# Take the first row as column headers
data.columns = data.iloc[0] 

# Remove the first row
data = data[1:]

# Set new index
data.index = index 

# Parse the index to datetime
data.index = pd.to_datetime(data.index, format='%B_%Y')

data

# Replace missing values ('-') with NaN
data.replace('-', np.nan, inplace=True)

# Identify columns with missing values
missing_columns = data.columns[data.isnull().sum() > 0]

# Show the number of missing values per column
print("Column with missing values:")
print(data[missing_columns].isnull().sum())

# Drop columns with missing values
data = data.drop(columns=missing_columns)

data.columns

Column with missing values:
0
PAPUA BARAT DAYA    60
PAPUA SELATAN       60
PAPUA TENGAH        60
PAPUA PEGUNUNGAN    60
dtype: int64

Index(['ACEH', 'SUMATERA UTARA', 'SUMATERA BARAT', 'RIAU', 'JAMBI',
       'SUMATERA SELATAN', 'BENGKULU', 'LAMPUNG', 'KEP. BANGKA BELITUNG',
       'KEP. RIAU', 'DKI JAKARTA', 'JAWA BARAT', 'JAWA TENGAH',
       'DI YOGYAKARTA', 'JAWA TIMUR', 'BANTEN', 'BALI', 'NUSA TENGGARA BARAT',
       'NUSA TENGGARA TIMUR', 'KALIMANTAN BARAT', 'KALIMANTAN TENGAH',
       'KALIMANTAN SELATAN', 'KALIMANTAN TIMUR', 'KALIMANTAN UTARA',
       'SULAWESI UTARA', 'SULAWESI TENGAH', 'SULAWESI SELATAN',
       'SULAWESI TENGGARA', 'GORONTALO', 'SULAWESI BARAT', 'MALUKU',
       'MALUKU UTARA', 'PAPUA BARAT', 'PAPUA', 'INDONESIA'],
      dtype='object', name=0)

# Convert the dataset to numeric
data = data.apply(pd.to_numeric)

# Drop unnecessary feature (INDONESIA)
data.drop(columns="INDONESIA", inplace=True)

# Display the final dataset features and instances
print("Total instance and features:", data.shape)
print("\nList of features:\n", data.columns)
print("\nList of instance (time_series):\n", data.index)

Total instance and features: (66, 34)

List of features:
 Index(['ACEH', 'SUMATERA UTARA', 'SUMATERA BARAT', 'RIAU', 'JAMBI',
       'SUMATERA SELATAN', 'BENGKULU', 'LAMPUNG', 'KEP. BANGKA BELITUNG',
       'KEP. RIAU', 'DKI JAKARTA', 'JAWA BARAT', 'JAWA TENGAH',
       'DI YOGYAKARTA', 'JAWA TIMUR', 'BANTEN', 'BALI', 'NUSA TENGGARA BARAT',
       'NUSA TENGGARA TIMUR', 'KALIMANTAN BARAT', 'KALIMANTAN TENGAH',
       'KALIMANTAN SELATAN', 'KALIMANTAN TIMUR', 'KALIMANTAN UTARA',
       'SULAWESI UTARA', 'SULAWESI TENGAH', 'SULAWESI SELATAN',
       'SULAWESI TENGGARA', 'GORONTALO', 'SULAWESI BARAT', 'MALUKU',
       'MALUKU UTARA', 'PAPUA BARAT', 'PAPUA'],
      dtype='object', name=0)

List of instance (time_series):
 DatetimeIndex(['2019-01-01', '2019-02-01', '2019-03-01', '2019-04-01',
               '2019-05-01', '2019-06-01', '2019-07-01', '2019-08-01',
               '2019-09-01', '2019-10-01', '2019-11-01', '2019-12-01',
               '2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01',
               '2020-05-01', '2020-06-01', '2020-07-01', '2020-08-01',
               '2020-09-01', '2020-10-01', '2020-11-01', '2020-12-01',
               '2021-01-01', '2021-02-01', '2021-03-01', '2021-04-01',
               '2021-05-01', '2021-06-01', '2021-07-01', '2021-08-01',
               '2021-09-01', '2021-10-01', '2021-11-01', '2021-12-01',
               '2022-01-01', '2022-02-01', '2022-03-01', '2022-04-01',
               '2022-05-01', '2022-06-01', '2022-07-01', '2022-08-01',
               '2022-09-01', '2022-10-01', '2022-11-01', '2022-12-01',
               '2023-01-01', '2023-02-01', '2023-03-01', '2023-04-01',
               '2023-05-01', '2023-06-01', '2023-07-01', '2023-08-01',
               '2023-09-01', '2023-10-01', '2023-11-01', '2023-12-01',
               '2024-01-01', '2024-02-01', '2024-03-01', '2024-04-01',
               '2024-05-01', '2024-06-01'],
              dtype='datetime64[ns]', freq=None)

# Reset index
data.reset_index(inplace=True)  

# Rename the index column to 'time_series'
data.rename(columns={'index': 'time_series'}, inplace=True)  

# Save the dataset to CSV
data.to_csv("cleaned_data.csv", index=False)

# Load the dataset
data = pd.read_csv("cleaned_data.csv")

# Set the 'time_series' column as the index
data.set_index('time_series', inplace=True)

data

training_data = data.loc['2019-01-01':'2023-12-01']
training_data

test_data = data.loc['2024-01-01':'2024-06-01']
test_data

from statsmodels.graphics.tsaplots import plot_acf

def make_acf_plot(series):
    plot_acf(series, lags=36, title='Autocorrelation Plot for {}'.format(series.name))

provinces = data.columns

for province in provinces:
    make_acf_plot(training_data[province])

c:\Users\Ryu\anaconda3\Lib\site-packages\statsmodels\graphics\utils.py:55: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
  fig = plt.figure()

from statsmodels.graphics.tsaplots import plot_pacf

def make_pacf_plot(series, lags):
    plot_pacf(series, lags=lags, title='Partial Autocorrelation Plot for {}'.format(series.name))

for province in provinces: 
    max_lags = min(36, len(province) // 2)
    make_pacf_plot(training_data[province], max_lags)

c:\Users\Ryu\anaconda3\Lib\site-packages\statsmodels\graphics\utils.py:55: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
  fig = plt.figure()

from statsmodels.tsa.stattools import adfuller

# Function to check stationarity using ADF test
def check_stationarity_adf(series, index, stationary_provinces, non_stationary_provinces):
    result = adfuller(series) 
    print(f"{index}. PROVINCE: {series.name}")
    print("   a. ADF Statistic:", result[0])
    print("   b. p-value:", result[1])
    if result[1] <= 0.05:
        print(f"   c. Stationarity: {series.name} is Stationary")
        stationary_provinces.append(series.name)  # Add to stationary list
    else:
        print(f"   c. Stationarity: {series.name} is Non-Stationary")
        non_stationary_provinces.append(series.name)  # Add to non-stationary list
    print("   d. Number of Lags:", result[2])
    print("   e. Number of Observations Used:", result[3])
    print("   f. Critical Values:")
    for key, value in result[4].items():
        print(f"   \t{key}: {value}")
    print(("=" * 50) + "\n")

# Lists to store stationary and non-stationary provinces
stationary_provinces = []
non_stationary_provinces = []

# Apply the check_stationarity_adf function to each province
i = 1
for province in data.columns:
    check_stationarity_adf(training_data[province], i, stationary_provinces, non_stationary_provinces)
    i += 1

1. PROVINCE: ACEH
   a. ADF Statistic: -2.6710185203108607
   b. p-value: 0.07918899093143678
   c. Stationarity: ACEH is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

2. PROVINCE: SUMATERA UTARA
   a. ADF Statistic: -3.641050892386336
   b. p-value: 0.005023016623325027
   c. Stationarity: SUMATERA UTARA is Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

3. PROVINCE: SUMATERA BARAT
   a. ADF Statistic: -3.027760309318199
   b. p-value: 0.03238201252177422
   c. Stationarity: SUMATERA BARAT is Stationary
   d. Number of Lags: 1
   e. Number of Observations Used: 58
   f. Critical Values:
   	1%: -3.548493559596539
   	5%: -2.912836594776334
   	10%: -2.594129155766944
==================================================

4. PROVINCE: RIAU
   a. ADF Statistic: -2.339328533334447
   b. p-value: 0.15959718785926064
   c. Stationarity: RIAU is Non-Stationary
   d. Number of Lags: 1
   e. Number of Observations Used: 58
   f. Critical Values:
   	1%: -3.548493559596539
   	5%: -2.912836594776334
   	10%: -2.594129155766944
==================================================

5. PROVINCE: JAMBI
   a. ADF Statistic: -3.4413152215306777
   b. p-value: 0.009628528086471496
   c. Stationarity: JAMBI is Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

6. PROVINCE: SUMATERA SELATAN
   a. ADF Statistic: -3.7966475064460465
   b. p-value: 0.0029418022353743896
   c. Stationarity: SUMATERA SELATAN is Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

7. PROVINCE: BENGKULU
   a. ADF Statistic: -2.4182335669918245
   b. p-value: 0.1366561115599484
   c. Stationarity: BENGKULU is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

8. PROVINCE: LAMPUNG
   a. ADF Statistic: -4.690990814504573
   b. p-value: 8.749399120685386e-05
   c. Stationarity: LAMPUNG is Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

9. PROVINCE: KEP. BANGKA BELITUNG
   a. ADF Statistic: -2.1514513677531246
   b. p-value: 0.2243349565012021
   c. Stationarity: KEP. BANGKA BELITUNG is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

10. PROVINCE: KEP. RIAU
   a. ADF Statistic: -2.612790568623438
   b. p-value: 0.09038242711224997
   c. Stationarity: KEP. RIAU is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

11. PROVINCE: DKI JAKARTA
   a. ADF Statistic: -1.6836826947450272
   b. p-value: 0.4395386444395364
   c. Stationarity: DKI JAKARTA is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

12. PROVINCE: JAWA BARAT
   a. ADF Statistic: -3.077859603812104
   b. p-value: 0.028235658904560344
   c. Stationarity: JAWA BARAT is Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

13. PROVINCE: JAWA TENGAH
   a. ADF Statistic: -3.7808535418814055
   b. p-value: 0.0031093965600096267
   c. Stationarity: JAWA TENGAH is Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

14. PROVINCE: DI YOGYAKARTA
   a. ADF Statistic: -3.804538339098204
   b. p-value: 0.0028612216647321454
   c. Stationarity: DI YOGYAKARTA is Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

15. PROVINCE: JAWA TIMUR
   a. ADF Statistic: -2.0485571752382805
   b. p-value: 0.2656963571084213
   c. Stationarity: JAWA TIMUR is Non-Stationary
   d. Number of Lags: 1
   e. Number of Observations Used: 58
   f. Critical Values:
   	1%: -3.548493559596539
   	5%: -2.912836594776334
   	10%: -2.594129155766944
==================================================

16. PROVINCE: BANTEN
   a. ADF Statistic: -3.0213835515011582
   b. p-value: 0.032945128666594825
   c. Stationarity: BANTEN is Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

17. PROVINCE: BALI
   a. ADF Statistic: -1.8090932798327368
   b. p-value: 0.3759565695603854
   c. Stationarity: BALI is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

18. PROVINCE: NUSA TENGGARA BARAT
   a. ADF Statistic: -2.199211026853943
   b. p-value: 0.20653193320898494
   c. Stationarity: NUSA TENGGARA BARAT is Non-Stationary
   d. Number of Lags: 1
   e. Number of Observations Used: 58
   f. Critical Values:
   	1%: -3.548493559596539
   	5%: -2.912836594776334
   	10%: -2.594129155766944
==================================================

19. PROVINCE: NUSA TENGGARA TIMUR
   a. ADF Statistic: -3.3566985947313377
   b. p-value: 0.012526617208591993
   c. Stationarity: NUSA TENGGARA TIMUR is Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

20. PROVINCE: KALIMANTAN BARAT
   a. ADF Statistic: -2.627592656292735
   b. p-value: 0.08742746046355293
   c. Stationarity: KALIMANTAN BARAT is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

21. PROVINCE: KALIMANTAN TENGAH
   a. ADF Statistic: -1.9834503302108013
   b. p-value: 0.29387636217954116
   c. Stationarity: KALIMANTAN TENGAH is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

22. PROVINCE: KALIMANTAN SELATAN
   a. ADF Statistic: -2.202627919206674
   b. p-value: 0.20529305674002185
   c. Stationarity: KALIMANTAN SELATAN is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

23. PROVINCE: KALIMANTAN TIMUR
   a. ADF Statistic: -1.727324034157437
   b. p-value: 0.41709213193376937
   c. Stationarity: KALIMANTAN TIMUR is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

24. PROVINCE: KALIMANTAN UTARA
   a. ADF Statistic: -2.4305054684521687
   b. p-value: 0.1333108975464708
   c. Stationarity: KALIMANTAN UTARA is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

25. PROVINCE: SULAWESI UTARA
   a. ADF Statistic: -1.5387773340473792
   b. p-value: 0.514249040182932
   c. Stationarity: SULAWESI UTARA is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

26. PROVINCE: SULAWESI TENGAH
   a. ADF Statistic: -1.564988024666247
   b. p-value: 0.5011533121191761
   c. Stationarity: SULAWESI TENGAH is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

27. PROVINCE: SULAWESI SELATAN
   a. ADF Statistic: -2.3407846462949733
   b. p-value: 0.15915118577115633
   c. Stationarity: SULAWESI SELATAN is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

28. PROVINCE: SULAWESI TENGGARA
   a. ADF Statistic: -1.860754094346674
   b. p-value: 0.35074274116989074
   c. Stationarity: SULAWESI TENGGARA is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

29. PROVINCE: GORONTALO
   a. ADF Statistic: -1.5840773077653814
   b. p-value: 0.4915868931562521
   c. Stationarity: GORONTALO is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

30. PROVINCE: SULAWESI BARAT
   a. ADF Statistic: -1.8899339923291565
   b. p-value: 0.33681119469576576
   c. Stationarity: SULAWESI BARAT is Non-Stationary
   d. Number of Lags: 3
   e. Number of Observations Used: 56
   f. Critical Values:
   	1%: -3.552928203580539
   	5%: -2.9147306250000002
   	10%: -2.595137155612245
==================================================

31. PROVINCE: MALUKU
   a. ADF Statistic: -3.157727868497206
   b. p-value: 0.022565694044091495
   c. Stationarity: MALUKU is Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

32. PROVINCE: MALUKU UTARA
   a. ADF Statistic: -1.3804271529592522
   b. p-value: 0.5916227900873192
   c. Stationarity: MALUKU UTARA is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

33. PROVINCE: PAPUA BARAT
   a. ADF Statistic: -2.4475441169750076
   b. p-value: 0.12876441996031857
   c. Stationarity: PAPUA BARAT is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

34. PROVINCE: PAPUA
   a. ADF Statistic: -2.1985390596052574
   b. p-value: 0.2067761211877392
   c. Stationarity: PAPUA is Non-Stationary
   d. Number of Lags: 0
   e. Number of Observations Used: 59
   f. Critical Values:
   	1%: -3.5463945337644063
   	5%: -2.911939409384601
   	10%: -2.5936515282964665
==================================================

# Print the results
print("Stationary Provinces:")
print(", ".join(stationary_provinces))
print("\nNon-Stationary Provinces:")
print(", ".join(non_stationary_provinces))

Stationary Provinces:
SUMATERA UTARA, SUMATERA BARAT, JAMBI, SUMATERA SELATAN, LAMPUNG, JAWA BARAT, JAWA TENGAH, DI YOGYAKARTA, BANTEN, NUSA TENGGARA TIMUR, MALUKU

Non-Stationary Provinces:
ACEH, RIAU, BENGKULU, KEP. BANGKA BELITUNG, KEP. RIAU, DKI JAKARTA, JAWA TIMUR, BALI, NUSA TENGGARA BARAT, KALIMANTAN BARAT, KALIMANTAN TENGAH, KALIMANTAN SELATAN, KALIMANTAN TIMUR, KALIMANTAN UTARA, SULAWESI UTARA, SULAWESI TENGAH, SULAWESI SELATAN, SULAWESI TENGGARA, GORONTALO, SULAWESI BARAT, MALUKU UTARA, PAPUA BARAT, PAPUA

data = pd.read_csv("cleaned_data.csv")

# Set the 'time_series' column as the index
data.set_index('time_series', inplace=True)

data

data.describe().T.round(2)

# Calculating the mean for each feature
mean = data.mean().round(2)

# Creating a mean plot
plt.figure(figsize=(12, 6))
plt.plot(mean, marker='o', color='r')

# Adding numbers to each point on the plot
for i, value in enumerate(mean):
    plt.text(i, value, f'{value/1e6:.2f}', ha='center', va='bottom', fontsize=10)

plt.title('Mean Number of Domestic Tourist Visits per Province (January 2019 - June 2024)')
plt.xlabel('Province')
plt.ylabel('Mean (millions)')
plt.xticks(rotation=90)
plt.grid()
plt.tight_layout()
plt.show()

# Calculating the median for each feature
median = data.median().round(2)

# Creating a median plot
plt.figure(figsize=(12, 6))
plt.plot(median.index, median / 1e6, marker='o', color='blue', linestyle='-', label='Median Visits')

# Adding numbers to each point on the plot
for i, value in enumerate(median):
    plt.text(i, value / 1e6, f'{value / 1e6:.2f}', ha='center', va='bottom', fontsize=9, color='darkblue')

# Adding labels and title
plt.title('Median Number of Domestic Tourist Visits per Province (January 2019 - June 2024)', fontsize=14)
plt.xlabel('Province', fontsize=12)
plt.ylabel('Median (in millions)', fontsize=12)
plt.xticks(rotation=90, fontsize=10)
plt.yticks(fontsize=10)
plt.grid(alpha=0.3)
plt.legend()

# Show the plot
plt.show()

# Calculating the standard deviation
std = data.std().round(2)

# Creating a standard deviation plot
plt.figure(figsize=(12, 6))
plt.plot(std, marker='o', color='r')

# Adding numbers to each point on the plot
for i, value in enumerate(std):
    plt.text(i, value, f'{value/1e6:.2f}', ha='center', va='bottom', fontsize=10)

plt.title('Standard Deviation of Domestic Tourist Visits per Province (January 2019 - June 2024)')
plt.xlabel('Province')
plt.ylabel('Standard Deviation (millions)')
plt.xticks(rotation=90)
plt.grid()
plt.show()

# Calculating the min and max value for each feature
min_data = data.min()
max_data = data.max()

# Creating a min and max plot
plt.figure(figsize=(10, 6))
plt.plot(min_data, marker='o', color='red', label='Min')
plt.plot(max_data, marker='o', color='blue', label='Max')
plt.title('Min and Max Value of Domestic Tourist Visits per Province (January 2019 - June 2024))')
plt.xlabel('Province')
plt.ylabel('Trips (millions)')
plt.xticks(rotation=90)
plt.legend()
plt.grid(True)
plt.show()

import warnings
from statsmodels.tsa.arima.model import ARIMA
from sklearn.metrics import mean_absolute_percentage_error, mean_absolute_error

# Suppress ARIMA warnings
warnings.filterwarnings("ignore")

# List to store the predictions
all_provinces_predictions = pd.DataFrame()

# Dictionary to store the good prediction provinces
good_predictions = {}

# Initialize provinces
provinces = data.columns

# Define the prediction period
start = len(training_data)
end = len(training_data) + len(test_data) - 1

params = [(p, d, q) for p in range(5) for d in range(2) for q in range(5)]

params

[(0, 0, 0),
 (0, 0, 1),
 (0, 0, 2),
 (0, 0, 3),
 (0, 0, 4),
 (0, 1, 0),
 (0, 1, 1),
 (0, 1, 2),
 (0, 1, 3),
 (0, 1, 4),
 (1, 0, 0),
 (1, 0, 1),
 (1, 0, 2),
 (1, 0, 3),
 (1, 0, 4),
 (1, 1, 0),
 (1, 1, 1),
 (1, 1, 2),
 (1, 1, 3),
 (1, 1, 4),
 (2, 0, 0),
 (2, 0, 1),
 (2, 0, 2),
 (2, 0, 3),
 (2, 0, 4),
 (2, 1, 0),
 (2, 1, 1),
 (2, 1, 2),
 (2, 1, 3),
 (2, 1, 4),
 (3, 0, 0),
 (3, 0, 1),
 (3, 0, 2),
 (3, 0, 3),
 (3, 0, 4),
 (3, 1, 0),
 (3, 1, 1),
 (3, 1, 2),
 (3, 1, 3),
 (3, 1, 4),
 (4, 0, 0),
 (4, 0, 1),
 (4, 0, 2),
 (4, 0, 3),
 (4, 0, 4),
 (4, 1, 0),
 (4, 1, 1),
 (4, 1, 2),
 (4, 1, 3),
 (4, 1, 4)]

def make_arima_prediction(train, test):
    lowest_mape = float("inf")
    best_param = None
    best_prediction = None
    lowest_mae = float("inf")

    for param in params:
        arima = ARIMA(endog=train, order=param, seasonal_order=(1, 0, 0, 12))
        arima = arima.fit()
        
        prediction = arima.predict(start=start, end=end, typ="levels")
        
        mape = mean_absolute_percentage_error(test, prediction) * 100
        mae = mean_absolute_error(test, prediction)        
        
        print(f'ARIMA{param}')
        print(f'MAPE: {mape.round(2)}%')
        print(f'MAE: {mae.round(2)}')
        print("=" * 50)
        
        if mape < lowest_mape:
            lowest_mape = mape
            lowest_mae = mae
            best_param = param
            best_prediction = prediction
            
            if lowest_mape <= 20:
                good_predictions[train.name] = lowest_mape 
            
    return best_param, lowest_mape, lowest_mae, best_prediction

def plot_predictions_vs_actual(prediction_series, actual_series, xlabel="Time", ylabel="Values"):
    # Check if the index of prediction_series is already a DatetimeIndex, if not, convert it
    if isinstance(prediction_series.index, pd.PeriodIndex):
        prediction_series.index = prediction_series.index.to_timestamp()  # Converts PeriodIndex to DatetimeIndex
    elif not isinstance(prediction_series.index, pd.DatetimeIndex):
        prediction_series.index = pd.to_datetime(prediction_series.index)  # If it's neither, convert it to DatetimeIndex

    # Check if the index of actual_series is already a DatetimeIndex, if not, convert it
    if isinstance(actual_series.index, pd.PeriodIndex):
        actual_series.index = actual_series.index.to_timestamp()  # Converts PeriodIndex to DatetimeIndex
    elif not isinstance(actual_series.index, pd.DatetimeIndex):
        actual_series.index = pd.to_datetime(actual_series.index)  # If it's neither, convert it to DatetimeIndex

    # Plot the data
    plt.figure(figsize=(8, 4))
    prediction_series.plot(legend=True, label='Predictions')
    actual_series.plot(legend=True, label='Actual')
    
    # Customize the plot
    plt.title("Prediction vs Actual " + actual_series.name)
    plt.xlabel(xlabel)
    plt.ylabel(ylabel)
    plt.show()

train_aceh = training_data["ACEH"]
test_aceh = test_data["ACEH"]

best_param_aceh, lowest_mape_aceh, lowest_mae_aceh, prediction_aceh = make_arima_prediction(train_aceh, test_aceh)
    
all_provinces_predictions['ACEH'] = prediction_aceh

ARIMA(0, 0, 0)
MAPE: 31.72%
MAE: 338747.81
==================================================
ARIMA(0, 0, 1)
MAPE: 29.85%
MAE: 318106.18
==================================================
ARIMA(0, 0, 2)
MAPE: 31.31%
MAE: 331444.92
==================================================
ARIMA(0, 0, 3)
MAPE: 34.04%
MAE: 360208.2
==================================================
ARIMA(0, 0, 4)
MAPE: 35.36%
MAE: 363933.1
==================================================
ARIMA(0, 1, 0)
MAPE: 63.78%
MAE: 636131.83
==================================================
ARIMA(0, 1, 1)
MAPE: 25.57%
MAE: 248305.97
==================================================
ARIMA(0, 1, 2)
MAPE: 29.51%
MAE: 286487.71
==================================================
ARIMA(0, 1, 3)
MAPE: 27.93%
MAE: 271751.96
==================================================
ARIMA(0, 1, 4)
MAPE: 27.36%
MAE: 265776.65
==================================================
ARIMA(1, 0, 0)
MAPE: 24.83%
MAE: 243150.51
==================================================
ARIMA(1, 0, 1)
MAPE: 17.98%
MAE: 177010.23
==================================================
ARIMA(1, 0, 2)
MAPE: 25.41%
MAE: 244370.44
==================================================
ARIMA(1, 0, 3)
MAPE: 24.07%
MAE: 232536.39
==================================================
ARIMA(1, 0, 4)
MAPE: 24.81%
MAE: 238568.67
==================================================
ARIMA(1, 1, 0)
MAPE: 34.93%
MAE: 341719.09
==================================================
ARIMA(1, 1, 1)
MAPE: 34.34%
MAE: 335705.15
==================================================
ARIMA(1, 1, 2)
MAPE: 31.62%
MAE: 308039.3
==================================================
ARIMA(1, 1, 3)
MAPE: 27.82%
MAE: 270507.6
==================================================
ARIMA(1, 1, 4)
MAPE: 29.22%
MAE: 285646.96
==================================================
ARIMA(2, 0, 0)
MAPE: 19.23%
MAE: 186778.0
==================================================
ARIMA(2, 0, 1)
MAPE: 20.43%
MAE: 197335.83
==================================================
ARIMA(2, 0, 2)
MAPE: 24.43%
MAE: 234523.36
==================================================
ARIMA(2, 0, 3)
MAPE: 24.66%
MAE: 238127.36
==================================================
ARIMA(2, 0, 4)
MAPE: 25.88%
MAE: 249676.76
==================================================
ARIMA(2, 1, 0)
MAPE: 34.51%
MAE: 337451.19
==================================================
ARIMA(2, 1, 1)
MAPE: 19.29%
MAE: 188822.34
==================================================
ARIMA(2, 1, 2)
MAPE: 19.72%
MAE: 193492.33
==================================================
ARIMA(2, 1, 3)
MAPE: 26.49%
MAE: 257712.65
==================================================
ARIMA(2, 1, 4)
MAPE: 26.8%
MAE: 262113.92
==================================================
ARIMA(3, 0, 0)
MAPE: 21.51%
MAE: 207032.69
==================================================
ARIMA(3, 0, 1)
MAPE: 18.87%
MAE: 181563.26
==================================================
ARIMA(3, 0, 2)
MAPE: 28.05%
MAE: 273862.91
==================================================
ARIMA(3, 0, 3)
MAPE: 23.47%
MAE: 230193.16
==================================================
ARIMA(3, 0, 4)
MAPE: 26.06%
MAE: 253195.32
==================================================
ARIMA(3, 1, 0)
MAPE: 30.68%
MAE: 298830.38
==================================================
ARIMA(3, 1, 1)
MAPE: 26.84%
MAE: 260013.19
==================================================
ARIMA(3, 1, 2)
MAPE: 25.74%
MAE: 254273.55
==================================================
ARIMA(3, 1, 3)
MAPE: 29.82%
MAE: 293614.2
==================================================
ARIMA(3, 1, 4)
MAPE: 19.31%
MAE: 193209.52
==================================================
ARIMA(4, 0, 0)
MAPE: 22.81%
MAE: 218746.36
==================================================
ARIMA(4, 0, 1)
MAPE: 22.84%
MAE: 219259.33
==================================================
ARIMA(4, 0, 2)
MAPE: 23.72%
MAE: 231704.73
==================================================
ARIMA(4, 0, 3)
MAPE: 24.5%
MAE: 238744.37
==================================================
ARIMA(4, 0, 4)
MAPE: 20.35%
MAE: 199487.9
==================================================
ARIMA(4, 1, 0)
MAPE: 24.28%
MAE: 242036.76
==================================================
ARIMA(4, 1, 1)
MAPE: 24.6%
MAE: 244359.45
==================================================
ARIMA(4, 1, 2)
MAPE: 30.14%
MAE: 292821.97
==================================================
ARIMA(4, 1, 3)
MAPE: 20.71%
MAE: 207253.37
==================================================
ARIMA(4, 1, 4)
MAPE: 27.48%
MAE: 270398.02
==================================================

print("Best parameters model for ACEH:", best_param_aceh)
print(f"with the lowest MAPE: {lowest_mape_aceh.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_aceh.round(2)}")

Best parameters model for ACEH: (1, 0, 1)
with the lowest MAPE: 17.98%
with the lowest MAE: 177010.23

plot_predictions_vs_actual(prediction_series=prediction_aceh, actual_series=test_aceh)

train_sumatera_utara = training_data["SUMATERA UTARA"]
test_sumatera_utara = test_data["SUMATERA UTARA"]

best_param_sumatera_utara, lowest_mape_sumatera_utara, lowest_mae_sumatera_utara, prediction_sumatera_utara = make_arima_prediction(train_sumatera_utara, test_sumatera_utara)

all_provinces_predictions['SUMATERA UTARA'] = prediction_sumatera_utara

ARIMA(0, 0, 0)
MAPE: 38.69%
MAE: 1369150.22
==================================================
ARIMA(0, 0, 1)
MAPE: 34.8%
MAE: 1213032.03
==================================================
ARIMA(0, 0, 2)
MAPE: 32.25%
MAE: 1129022.1
==================================================
ARIMA(0, 0, 3)
MAPE: 26.67%
MAE: 930054.78
==================================================
ARIMA(0, 0, 4)
MAPE: 24.16%
MAE: 840963.83
==================================================
ARIMA(0, 1, 0)
MAPE: 8.83%
MAE: 325799.85
==================================================
ARIMA(0, 1, 1)
MAPE: 24.94%
MAE: 891160.99
==================================================
ARIMA(0, 1, 2)
MAPE: 25.7%
MAE: 916638.84
==================================================
ARIMA(0, 1, 3)
MAPE: 26.33%
MAE: 938075.43
==================================================
ARIMA(0, 1, 4)
MAPE: 26.27%
MAE: 935898.58
==================================================
ARIMA(1, 0, 0)
MAPE: 19.93%
MAE: 694242.75
==================================================
ARIMA(1, 0, 1)
MAPE: 21.37%
MAE: 749336.46
==================================================
ARIMA(1, 0, 2)
MAPE: 20.35%
MAE: 713314.42
==================================================
ARIMA(1, 0, 3)
MAPE: 19.35%
MAE: 677558.16
==================================================
ARIMA(1, 0, 4)
MAPE: 16.21%
MAE: 566372.74
==================================================
ARIMA(1, 1, 0)
MAPE: 18.04%
MAE: 654532.26
==================================================
ARIMA(1, 1, 1)
MAPE: 25.71%
MAE: 916745.73
==================================================
ARIMA(1, 1, 2)
MAPE: 28.63%
MAE: 1016240.4
==================================================
ARIMA(1, 1, 3)
MAPE: 26.28%
MAE: 936538.45
==================================================
ARIMA(1, 1, 4)
MAPE: 25.57%
MAE: 911111.38
==================================================
ARIMA(2, 0, 0)
MAPE: 20.02%
MAE: 702898.12
==================================================
ARIMA(2, 0, 1)
MAPE: 20.85%
MAE: 731799.03
==================================================
ARIMA(2, 0, 2)
MAPE: 20.22%
MAE: 708958.17
==================================================
ARIMA(2, 0, 3)
MAPE: 19.41%
MAE: 679208.99
==================================================
ARIMA(2, 0, 4)
MAPE: 16.6%
MAE: 579933.28
==================================================
ARIMA(2, 1, 0)
MAPE: 21.3%
MAE: 766514.68
==================================================
ARIMA(2, 1, 1)
MAPE: 27.88%
MAE: 989673.9
==================================================
ARIMA(2, 1, 2)
MAPE: 28.6%
MAE: 1014981.02
==================================================
ARIMA(2, 1, 3)
MAPE: 32.95%
MAE: 1165868.73
==================================================
ARIMA(2, 1, 4)
MAPE: 28.26%
MAE: 1002980.37
==================================================
ARIMA(3, 0, 0)
MAPE: 20.52%
MAE: 720263.14
==================================================
ARIMA(3, 0, 1)
MAPE: 20.46%
MAE: 718775.71
==================================================
ARIMA(3, 0, 2)
MAPE: 18.79%
MAE: 656321.94
==================================================
ARIMA(3, 0, 3)
MAPE: 18.21%
MAE: 635941.88
==================================================
ARIMA(3, 0, 4)
MAPE: 18.52%
MAE: 645235.1
==================================================
ARIMA(3, 1, 0)
MAPE: 23.59%
MAE: 844221.48
==================================================
ARIMA(3, 1, 1)
MAPE: 26.28%
MAE: 935894.51
==================================================
ARIMA(3, 1, 2)
MAPE: 30.33%
MAE: 1079404.52
==================================================
ARIMA(3, 1, 3)
MAPE: 28.92%
MAE: 1025806.14
==================================================
ARIMA(3, 1, 4)
MAPE: 28.21%
MAE: 1000250.32
==================================================
ARIMA(4, 0, 0)
MAPE: 20.28%
MAE: 712340.07
==================================================
ARIMA(4, 0, 1)
MAPE: 20.06%
MAE: 705232.22
==================================================
ARIMA(4, 0, 2)
MAPE: 18.78%
MAE: 656902.93
==================================================
ARIMA(4, 0, 3)
MAPE: 18.96%
MAE: 662540.84
==================================================
ARIMA(4, 0, 4)
MAPE: 24.05%
MAE: 836262.8
==================================================
ARIMA(4, 1, 0)
MAPE: 28.88%
MAE: 1026302.49
==================================================
ARIMA(4, 1, 1)
MAPE: 28.43%
MAE: 1011246.27
==================================================
ARIMA(4, 1, 2)
MAPE: 27.69%
MAE: 985943.88
==================================================
ARIMA(4, 1, 3)
MAPE: 30.84%
MAE: 1096844.19
==================================================
ARIMA(4, 1, 4)
MAPE: 31.89%
MAE: 1135414.33
==================================================

print(f"Best parameters model for SUMATERA UTARA:", best_param_sumatera_utara)
print(f"with the lowest MAPE: {lowest_mape_sumatera_utara.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_sumatera_utara.round(2)}")

Best parameters model for SUMATERA UTARA: (0, 1, 0)
with the lowest MAPE: 8.83%
with the lowest MAE: 325799.85

plot_predictions_vs_actual(prediction_series=prediction_sumatera_utara, actual_series=test_sumatera_utara)

train_sumatera_barat = training_data["SUMATERA BARAT"]
test_sumatera_barat = test_data["SUMATERA BARAT"]

best_param_sumatera_barat, lowest_mape_sumatera_barat, lowest_mae_sumatera_barat, prediction_sumatera_barat = make_arima_prediction(train_sumatera_barat, test_sumatera_barat)

all_provinces_predictions['SUMATERA BARAT'] = prediction_sumatera_barat

ARIMA(0, 0, 0)
MAPE: 32.42%
MAE: 527420.53
==================================================
ARIMA(0, 0, 1)
MAPE: 28.37%
MAE: 462523.71
==================================================
ARIMA(0, 0, 2)
MAPE: 26.88%
MAE: 440030.29
==================================================
ARIMA(0, 0, 3)
MAPE: 22.88%
MAE: 380527.07
==================================================
ARIMA(0, 0, 4)
MAPE: 22.89%
MAE: 380766.92
==================================================
ARIMA(0, 1, 0)
MAPE: 15.39%
MAE: 257396.4
==================================================
ARIMA(0, 1, 1)
MAPE: 24.71%
MAE: 408444.19
==================================================
ARIMA(0, 1, 2)
MAPE: 24.37%
MAE: 402234.82
==================================================
ARIMA(0, 1, 3)
MAPE: 24.68%
MAE: 405844.46
==================================================
ARIMA(0, 1, 4)
MAPE: 32.46%
MAE: 524192.45
==================================================
ARIMA(1, 0, 0)
MAPE: 22.94%
MAE: 379415.1
==================================================
ARIMA(1, 0, 1)
MAPE: 22.34%
MAE: 371566.4
==================================================
ARIMA(1, 0, 2)
MAPE: 21.16%
MAE: 353590.74
==================================================
ARIMA(1, 0, 3)
MAPE: 22.98%
MAE: 382020.47
==================================================
ARIMA(1, 0, 4)
MAPE: 20.31%
MAE: 337513.95
==================================================
ARIMA(1, 1, 0)
MAPE: 20.33%
MAE: 341485.97
==================================================
ARIMA(1, 1, 1)
MAPE: 23.38%
MAE: 386581.14
==================================================
ARIMA(1, 1, 2)
MAPE: 29.98%
MAE: 487630.96
==================================================
ARIMA(1, 1, 3)
MAPE: 30.07%
MAE: 488966.53
==================================================
ARIMA(1, 1, 4)
MAPE: 32.83%
MAE: 531069.4
==================================================
ARIMA(2, 0, 0)
MAPE: 22.5%
MAE: 373933.59
==================================================
ARIMA(2, 0, 1)
MAPE: 21.9%
MAE: 364655.53
==================================================
ARIMA(2, 0, 2)
MAPE: 21.38%
MAE: 356450.93
==================================================
ARIMA(2, 0, 3)
MAPE: 23.11%
MAE: 383981.34
==================================================
ARIMA(2, 0, 4)
MAPE: 20.4%
MAE: 339419.38
==================================================
ARIMA(2, 1, 0)
MAPE: 21.71%
MAE: 363033.06
==================================================
ARIMA(2, 1, 1)
MAPE: 29.79%
MAE: 484418.6
==================================================
ARIMA(2, 1, 2)
MAPE: 30.59%
MAE: 496439.89
==================================================
ARIMA(2, 1, 3)
MAPE: 22.57%
MAE: 371594.15
==================================================
ARIMA(2, 1, 4)
MAPE: 33.51%
MAE: 540781.84
==================================================
ARIMA(3, 0, 0)
MAPE: 22.5%
MAE: 374020.76
==================================================
ARIMA(3, 0, 1)
MAPE: 22.51%
MAE: 374352.48
==================================================
ARIMA(3, 0, 2)
MAPE: 22.49%
MAE: 373154.98
==================================================
ARIMA(3, 0, 3)
MAPE: 20.63%
MAE: 341049.52
==================================================
ARIMA(3, 0, 4)
MAPE: 17.39%
MAE: 294908.03
==================================================
ARIMA(3, 1, 0)
MAPE: 23.18%
MAE: 383777.45
==================================================
ARIMA(3, 1, 1)
MAPE: 29.79%
MAE: 484403.26
==================================================
ARIMA(3, 1, 2)
MAPE: 35.26%
MAE: 570993.33
==================================================
ARIMA(3, 1, 3)
MAPE: 28.52%
MAE: 458953.52
==================================================
ARIMA(3, 1, 4)
MAPE: 31.28%
MAE: 506232.74
==================================================
ARIMA(4, 0, 0)
MAPE: 22.49%
MAE: 373611.77
==================================================
ARIMA(4, 0, 1)
MAPE: 22.58%
MAE: 375434.56
==================================================
ARIMA(4, 0, 2)
MAPE: 21.99%
MAE: 365066.35
==================================================
ARIMA(4, 0, 3)
MAPE: 20.91%
MAE: 348996.58
==================================================
ARIMA(4, 0, 4)
MAPE: 17.71%
MAE: 299150.69
==================================================
ARIMA(4, 1, 0)
MAPE: 27.0%
MAE: 444119.9
==================================================
ARIMA(4, 1, 1)
MAPE: 27.29%
MAE: 448170.42
==================================================
ARIMA(4, 1, 2)
MAPE: 28.01%
MAE: 459907.24
==================================================
ARIMA(4, 1, 3)
MAPE: 25.6%
MAE: 421012.52
==================================================
ARIMA(4, 1, 4)
MAPE: 46.3%
MAE: 730797.96
==================================================

print(f"Best parameters model for SUMATERA BARAT:", best_param_sumatera_barat)
print(f"with the lowest MAPE: {lowest_mape_sumatera_barat.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_sumatera_barat.round(2)}")

Best parameters model for SUMATERA BARAT: (0, 1, 0)
with the lowest MAPE: 15.39%
with the lowest MAE: 257396.4

plot_predictions_vs_actual(prediction_series=prediction_sumatera_barat, actual_series=test_sumatera_barat)

train_riau = training_data["RIAU"] 
test_riau = test_data["RIAU"]

best_param_riau, lowest_mape_riau, lowest_mae_riau, prediction_riau = make_arima_prediction(train_riau, test_riau)

all_provinces_predictions['RIAU'] = prediction_riau

ARIMA(0, 0, 0)
MAPE: 46.17%
MAE: 705927.38
==================================================
ARIMA(0, 0, 1)
MAPE: 41.99%
MAE: 641069.11
==================================================
ARIMA(0, 0, 2)
MAPE: 40.12%
MAE: 615238.22
==================================================
ARIMA(0, 0, 3)
MAPE: 34.34%
MAE: 528950.93
==================================================
ARIMA(0, 0, 4)
MAPE: 32.11%
MAE: 493688.9
==================================================
ARIMA(0, 1, 0)
MAPE: 12.32%
MAE: 194291.8
==================================================
ARIMA(0, 1, 1)
MAPE: 27.87%
MAE: 429284.56
==================================================
ARIMA(0, 1, 2)
MAPE: 28.48%
MAE: 438552.94
==================================================
ARIMA(0, 1, 3)
MAPE: 28.91%
MAE: 445452.25
==================================================
ARIMA(0, 1, 4)
MAPE: 28.92%
MAE: 445636.29
==================================================
ARIMA(1, 0, 0)
MAPE: 24.52%
MAE: 376985.16
==================================================
ARIMA(1, 0, 1)
MAPE: 26.53%
MAE: 407464.94
==================================================
ARIMA(1, 0, 2)
MAPE: 25.97%
MAE: 398970.81
==================================================
ARIMA(1, 0, 3)
MAPE: 24.41%
MAE: 375602.53
==================================================
ARIMA(1, 0, 4)
MAPE: 20.2%
MAE: 312144.93
==================================================
ARIMA(1, 1, 0)
MAPE: 22.74%
MAE: 352013.62
==================================================
ARIMA(1, 1, 1)
MAPE: 28.46%
MAE: 438255.12
==================================================
ARIMA(1, 1, 2)
MAPE: 28.59%
MAE: 440165.15
==================================================
ARIMA(1, 1, 3)
MAPE: 28.9%
MAE: 445218.8
==================================================
ARIMA(1, 1, 4)
MAPE: 28.13%
MAE: 434590.26
==================================================
ARIMA(2, 0, 0)
MAPE: 25.51%
MAE: 392152.22
==================================================
ARIMA(2, 0, 1)
MAPE: 26.51%
MAE: 407157.49
==================================================
ARIMA(2, 0, 2)
MAPE: 25.92%
MAE: 398118.16
==================================================
ARIMA(2, 0, 3)
MAPE: 24.65%
MAE: 378761.87
==================================================
ARIMA(2, 0, 4)
MAPE: 20.57%
MAE: 318169.65
==================================================
ARIMA(2, 1, 0)
MAPE: 25.03%
MAE: 386434.66
==================================================
ARIMA(2, 1, 1)
MAPE: 28.4%
MAE: 437384.03
==================================================
ARIMA(2, 1, 2)
MAPE: 30.21%
MAE: 464487.96
==================================================
ARIMA(2, 1, 3)
MAPE: 25.48%
MAE: 397233.41
==================================================
ARIMA(2, 1, 4)
MAPE: 25.89%
MAE: 403543.68
==================================================
ARIMA(3, 0, 0)
MAPE: 25.87%
MAE: 397525.94
==================================================
ARIMA(3, 0, 1)
MAPE: 25.79%
MAE: 396455.25
==================================================
ARIMA(3, 0, 2)
MAPE: 23.74%
MAE: 366508.35
==================================================
ARIMA(3, 0, 3)
MAPE: 22.82%
MAE: 351301.4
==================================================
ARIMA(3, 0, 4)
MAPE: 20.9%
MAE: 322540.38
==================================================
ARIMA(3, 1, 0)
MAPE: 26.93%
MAE: 414757.64
==================================================
ARIMA(3, 1, 1)
MAPE: 28.94%
MAE: 445493.72
==================================================
ARIMA(3, 1, 2)
MAPE: 30.59%
MAE: 471223.27
==================================================
ARIMA(3, 1, 3)
MAPE: 25.72%
MAE: 400855.72
==================================================
ARIMA(3, 1, 4)
MAPE: 25.7%
MAE: 400970.49
==================================================
ARIMA(4, 0, 0)
MAPE: 26.16%
MAE: 401780.8
==================================================
ARIMA(4, 0, 1)
MAPE: 25.87%
MAE: 397737.35
==================================================
ARIMA(4, 0, 2)
MAPE: 23.88%
MAE: 367341.46
==================================================
ARIMA(4, 0, 3)
MAPE: 22.95%
MAE: 353607.62
==================================================
ARIMA(4, 0, 4)
MAPE: 20.23%
MAE: 312542.18
==================================================
ARIMA(4, 1, 0)
MAPE: 30.6%
MAE: 472238.39
==================================================
ARIMA(4, 1, 1)
MAPE: 30.39%
MAE: 469160.96
==================================================
ARIMA(4, 1, 2)
MAPE: 29.6%
MAE: 457917.62
==================================================
ARIMA(4, 1, 3)
MAPE: 31.72%
MAE: 488714.9
==================================================
ARIMA(4, 1, 4)
MAPE: 29.55%
MAE: 456994.91
==================================================

print(f"Best parameters model for RIAU:", best_param_riau)
print(f"with the lowest MAPE: {lowest_mape_riau.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_riau.round(2)}")

Best parameters model for RIAU: (0, 1, 0)
with the lowest MAPE: 12.32%
with the lowest MAE: 194291.8

plot_predictions_vs_actual(prediction_series=prediction_riau, actual_series=test_riau)

train_jambi = training_data["JAMBI"]
test_jambi = test_data["JAMBI"]

best_param_jambi, lowest_mape_jambi, lowest_mae_jambi, prediction_jambi = make_arima_prediction(train_jambi, test_jambi)

all_provinces_predictions['JAMBI'] = prediction_jambi

ARIMA(0, 0, 0)
MAPE: 47.03%
MAE: 328045.31
==================================================
ARIMA(0, 0, 1)
MAPE: 42.94%
MAE: 299989.22
==================================================
ARIMA(0, 0, 2)
MAPE: 40.26%
MAE: 283516.28
==================================================
ARIMA(0, 0, 3)
MAPE: 35.99%
MAE: 255955.44
==================================================
ARIMA(0, 0, 4)
MAPE: 26.71%
MAE: 191978.54
==================================================
ARIMA(0, 1, 0)
MAPE: 11.05%
MAE: 85274.4
==================================================
ARIMA(0, 1, 1)
MAPE: 20.69%
MAE: 150838.63
==================================================
ARIMA(0, 1, 2)
MAPE: 21.47%
MAE: 156082.49
==================================================
ARIMA(0, 1, 3)
MAPE: 24.61%
MAE: 177990.19
==================================================
ARIMA(0, 1, 4)
MAPE: 25.08%
MAE: 181999.47
==================================================
ARIMA(1, 0, 0)
MAPE: 17.98%
MAE: 130628.22
==================================================
ARIMA(1, 0, 1)
MAPE: 19.96%
MAE: 143301.52
==================================================
ARIMA(1, 0, 2)
MAPE: 16.1%
MAE: 117300.27
==================================================
ARIMA(1, 0, 3)
MAPE: 11.94%
MAE: 89002.07
==================================================
ARIMA(1, 0, 4)
MAPE: 11.03%
MAE: 82970.23
==================================================
ARIMA(1, 1, 0)
MAPE: 17.99%
MAE: 132648.46
==================================================
ARIMA(1, 1, 1)
MAPE: 24.7%
MAE: 178177.06
==================================================
ARIMA(1, 1, 2)
MAPE: 29.9%
MAE: 213710.59
==================================================
ARIMA(1, 1, 3)
MAPE: 25.07%
MAE: 181536.06
==================================================
ARIMA(1, 1, 4)
MAPE: 24.22%
MAE: 176380.86
==================================================
ARIMA(2, 0, 0)
MAPE: 19.71%
MAE: 141607.27
==================================================
ARIMA(2, 0, 1)
MAPE: 19.63%
MAE: 141081.98
==================================================
ARIMA(2, 0, 2)
MAPE: 14.06%
MAE: 103339.1
==================================================
ARIMA(2, 0, 3)
MAPE: 12.21%
MAE: 91031.6
==================================================
ARIMA(2, 0, 4)
MAPE: 9.94%
MAE: 75176.5
==================================================
ARIMA(2, 1, 0)
MAPE: 18.3%
MAE: 134717.57
==================================================
ARIMA(2, 1, 1)
MAPE: 29.92%
MAE: 213944.0
==================================================
ARIMA(2, 1, 2)
MAPE: 30.07%
MAE: 215081.54
==================================================
ARIMA(2, 1, 3)
MAPE: 25.57%
MAE: 184745.06
==================================================
ARIMA(2, 1, 4)
MAPE: 24.2%
MAE: 176209.73
==================================================
ARIMA(3, 0, 0)
MAPE: 19.46%
MAE: 139954.68
==================================================
ARIMA(3, 0, 1)
MAPE: 20.03%
MAE: 143922.89
==================================================
ARIMA(3, 0, 2)
MAPE: 17.72%
MAE: 129305.12
==================================================
ARIMA(3, 0, 3)
MAPE: 13.08%
MAE: 97665.85
==================================================
ARIMA(3, 0, 4)
MAPE: 13.55%
MAE: 100876.18
==================================================
ARIMA(3, 1, 0)
MAPE: 20.44%
MAE: 149103.02
==================================================
ARIMA(3, 1, 1)
MAPE: 24.68%
MAE: 178530.36
==================================================
ARIMA(3, 1, 2)
MAPE: 22.01%
MAE: 160033.21
==================================================
ARIMA(3, 1, 3)
MAPE: 31.47%
MAE: 225358.52
==================================================
ARIMA(3, 1, 4)
MAPE: 30.94%
MAE: 221969.85
==================================================
ARIMA(4, 0, 0)
MAPE: 15.84%
MAE: 115942.76
==================================================
ARIMA(4, 0, 1)
MAPE: 15.72%
MAE: 115080.03
==================================================
ARIMA(4, 0, 2)
MAPE: 18.62%
MAE: 135145.59
==================================================
ARIMA(4, 0, 3)
MAPE: 11.91%
MAE: 89486.09
==================================================
ARIMA(4, 0, 4)
MAPE: 12.36%
MAE: 92793.38
==================================================
ARIMA(4, 1, 0)
MAPE: 24.73%
MAE: 179789.28
==================================================
ARIMA(4, 1, 1)
MAPE: 23.17%
MAE: 168944.69
==================================================
ARIMA(4, 1, 2)
MAPE: 23.77%
MAE: 172974.45
==================================================
ARIMA(4, 1, 3)
MAPE: 22.66%
MAE: 165288.2
==================================================
ARIMA(4, 1, 4)
MAPE: 22.55%
MAE: 164614.6
==================================================

print(f"Best parameters model for JAMBI:", best_param_jambi)
print(f"with the lowest MAPE: {lowest_mape_jambi.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_jambi.round(2)}")

Best parameters model for JAMBI: (2, 0, 4)
with the lowest MAPE: 9.94%
with the lowest MAE: 75176.5

plot_predictions_vs_actual(prediction_series=prediction_jambi, actual_series=test_jambi)

train_sumatera_selatan = training_data["SUMATERA SELATAN"]
test_sumatera_selatan = test_data["SUMATERA SELATAN"]

best_param_sumatera_selatan, lowest_mape_sumatera_selatan, lowest_mae_sumatera_selatan, prediction_sumatera_selatan = make_arima_prediction(train_sumatera_selatan, test_sumatera_selatan)

all_provinces_predictions['SUMATERA SELATAN'] = prediction_sumatera_selatan

ARIMA(0, 0, 0)
MAPE: 44.74%
MAE: 652334.21
==================================================
ARIMA(0, 0, 1)
MAPE: 40.39%
MAE: 589221.33
==================================================
ARIMA(0, 0, 2)
MAPE: 36.49%
MAE: 537119.78
==================================================
ARIMA(0, 0, 3)
MAPE: 32.39%
MAE: 481381.97
==================================================
ARIMA(0, 0, 4)
MAPE: 28.48%
MAE: 420292.05
==================================================
ARIMA(0, 1, 0)
MAPE: 8.02%
MAE: 131300.34
==================================================
ARIMA(0, 1, 1)
MAPE: 20.94%
MAE: 318110.89
==================================================
ARIMA(0, 1, 2)
MAPE: 19.82%
MAE: 302467.12
==================================================
ARIMA(0, 1, 3)
MAPE: 22.69%
MAE: 344032.37
==================================================
ARIMA(0, 1, 4)
MAPE: 22.88%
MAE: 347115.16
==================================================
ARIMA(1, 0, 0)
MAPE: 16.92%
MAE: 256973.19
==================================================
ARIMA(1, 0, 1)
MAPE: 18.3%
MAE: 275829.5
==================================================
ARIMA(1, 0, 2)
MAPE: 14.85%
MAE: 227743.8
==================================================
ARIMA(1, 0, 3)
MAPE: 14.94%
MAE: 228968.51
==================================================
ARIMA(1, 0, 4)
MAPE: 13.96%
MAE: 215199.12
==================================================
ARIMA(1, 1, 0)
MAPE: 16.67%
MAE: 258079.08
==================================================
ARIMA(1, 1, 1)
MAPE: 17.83%
MAE: 274529.08
==================================================
ARIMA(1, 1, 2)
MAPE: 19.01%
MAE: 291025.55
==================================================
ARIMA(1, 1, 3)
MAPE: 23.0%
MAE: 348718.71
==================================================
ARIMA(1, 1, 4)
MAPE: 28.98%
MAE: 432597.44
==================================================
ARIMA(2, 0, 0)
MAPE: 18.2%
MAE: 274102.92
==================================================
ARIMA(2, 0, 1)
MAPE: 17.97%
MAE: 270666.7
==================================================
ARIMA(2, 0, 2)
MAPE: 15.18%
MAE: 232534.91
==================================================
ARIMA(2, 0, 3)
MAPE: 14.34%
MAE: 220824.81
==================================================
ARIMA(2, 0, 4)
MAPE: 13.65%
MAE: 210385.9
==================================================
ARIMA(2, 1, 0)
MAPE: 17.31%
MAE: 267189.99
==================================================
ARIMA(2, 1, 1)
MAPE: 28.55%
MAE: 425859.61
==================================================
ARIMA(2, 1, 2)
MAPE: 29.13%
MAE: 433939.54
==================================================
ARIMA(2, 1, 3)
MAPE: 23.66%
MAE: 357634.82
==================================================
ARIMA(2, 1, 4)
MAPE: 28.97%
MAE: 432577.96
==================================================
ARIMA(3, 0, 0)
MAPE: 17.57%
MAE: 264907.2
==================================================
ARIMA(3, 0, 1)
MAPE: 16.89%
MAE: 255379.41
==================================================
ARIMA(3, 0, 2)
MAPE: 15.26%
MAE: 233914.36
==================================================
ARIMA(3, 0, 3)
MAPE: 14.97%
MAE: 229899.39
==================================================
ARIMA(3, 0, 4)
MAPE: 11.65%
MAE: 179952.04
==================================================
ARIMA(3, 1, 0)
MAPE: 18.61%
MAE: 285101.07
==================================================
ARIMA(3, 1, 1)
MAPE: 28.77%
MAE: 428917.91
==================================================
ARIMA(3, 1, 2)
MAPE: 27.76%
MAE: 415889.86
==================================================
ARIMA(3, 1, 3)
MAPE: 28.42%
MAE: 425030.71
==================================================
ARIMA(3, 1, 4)
MAPE: 29.34%
MAE: 438051.36
==================================================
ARIMA(4, 0, 0)
MAPE: 16.44%
MAE: 250792.78
==================================================
ARIMA(4, 0, 1)
MAPE: 16.46%
MAE: 250953.82
==================================================
ARIMA(4, 0, 2)
MAPE: 15.79%
MAE: 240258.87
==================================================
ARIMA(4, 0, 3)
MAPE: 14.99%
MAE: 230350.43
==================================================
ARIMA(4, 0, 4)
MAPE: 11.81%
MAE: 185637.29
==================================================
ARIMA(4, 1, 0)
MAPE: 21.23%
MAE: 324782.95
==================================================
ARIMA(4, 1, 1)
MAPE: 19.89%
MAE: 305066.76
==================================================
ARIMA(4, 1, 2)
MAPE: 28.21%
MAE: 422457.33
==================================================
ARIMA(4, 1, 3)
MAPE: 28.21%
MAE: 422513.36
==================================================
ARIMA(4, 1, 4)
MAPE: 29.56%
MAE: 441857.03
==================================================

print(f"Best parameters model for SUMATERA SELATAN:", best_param_sumatera_selatan)
print(f"with the lowest MAPE: {lowest_mape_sumatera_selatan.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_sumatera_selatan.round(2)}")

Best parameters model for SUMATERA SELATAN: (0, 1, 0)
with the lowest MAPE: 8.02%
with the lowest MAE: 131300.34

plot_predictions_vs_actual(prediction_series=prediction_sumatera_selatan, actual_series=test_sumatera_selatan)

train_bengkulu = training_data["BENGKULU"] 
test_bengkulu = test_data["BENGKULU"]

best_param_bengkulu, lowest_mape_bengkulu, lowest_mae_bengkulu, prediction_bengkulu = make_arima_prediction(train_bengkulu, test_bengkulu)

all_provinces_predictions['BENGKULU'] = prediction_bengkulu

ARIMA(0, 0, 0)
MAPE: 40.26%
MAE: 149246.47
==================================================
ARIMA(0, 0, 1)
MAPE: 37.81%
MAE: 139653.37
==================================================
ARIMA(0, 0, 2)
MAPE: 39.3%
MAE: 142871.88
==================================================
ARIMA(0, 0, 3)
MAPE: 37.07%
MAE: 135628.67
==================================================
ARIMA(0, 0, 4)
MAPE: 42.27%
MAE: 145846.73
==================================================
ARIMA(0, 1, 0)
MAPE: 94.69%
MAE: 321690.73
==================================================
ARIMA(0, 1, 1)
MAPE: 38.84%
MAE: 126722.56
==================================================
ARIMA(0, 1, 2)
MAPE: 42.66%
MAE: 139991.77
==================================================
ARIMA(0, 1, 3)
MAPE: 37.43%
MAE: 122784.35
==================================================
ARIMA(0, 1, 4)
MAPE: 37.51%
MAE: 123271.65
==================================================
ARIMA(1, 0, 0)
MAPE: 33.13%
MAE: 111688.86
==================================================
ARIMA(1, 0, 1)
MAPE: 25.21%
MAE: 84598.44
==================================================
ARIMA(1, 0, 2)
MAPE: 32.4%
MAE: 103630.54
==================================================
ARIMA(1, 0, 3)
MAPE: 31.8%
MAE: 101920.43
==================================================
ARIMA(1, 0, 4)
MAPE: 32.89%
MAE: 106286.9
==================================================
ARIMA(1, 1, 0)
MAPE: 55.71%
MAE: 185291.93
==================================================
ARIMA(1, 1, 1)
MAPE: 45.13%
MAE: 148491.63
==================================================
ARIMA(1, 1, 2)
MAPE: 43.71%
MAE: 143546.44
==================================================
ARIMA(1, 1, 3)
MAPE: 37.33%
MAE: 122550.89
==================================================
ARIMA(1, 1, 4)
MAPE: 42.28%
MAE: 140297.79
==================================================
ARIMA(2, 0, 0)
MAPE: 26.78%
MAE: 87760.24
==================================================
ARIMA(2, 0, 1)
MAPE: 26.17%
MAE: 85969.1
==================================================
ARIMA(2, 0, 2)
MAPE: 32.28%
MAE: 103297.16
==================================================
ARIMA(2, 0, 3)
MAPE: 33.45%
MAE: 107258.65
==================================================
ARIMA(2, 0, 4)
MAPE: 33.56%
MAE: 108499.5
==================================================
ARIMA(2, 1, 0)
MAPE: 49.2%
MAE: 162558.7
==================================================
ARIMA(2, 1, 1)
MAPE: 53.58%
MAE: 176002.55
==================================================
ARIMA(2, 1, 2)
MAPE: 31.33%
MAE: 102999.0
==================================================
ARIMA(2, 1, 3)
MAPE: 42.63%
MAE: 139241.73
==================================================
ARIMA(2, 1, 4)
MAPE: 44.08%
MAE: 144142.75
==================================================
ARIMA(3, 0, 0)
MAPE: 26.02%
MAE: 85578.29
==================================================
ARIMA(3, 0, 1)
MAPE: 26.21%
MAE: 86940.05
==================================================
ARIMA(3, 0, 2)
MAPE: 40.11%
MAE: 133813.43
==================================================
ARIMA(3, 0, 3)
MAPE: 34.08%
MAE: 113912.13
==================================================
ARIMA(3, 0, 4)
MAPE: 34.65%
MAE: 115965.01
==================================================
ARIMA(3, 1, 0)
MAPE: 48.48%
MAE: 160118.17
==================================================
ARIMA(3, 1, 1)
MAPE: 31.04%
MAE: 102122.29
==================================================
ARIMA(3, 1, 2)
MAPE: 33.73%
MAE: 111867.45
==================================================
ARIMA(3, 1, 3)
MAPE: 33.81%
MAE: 112501.96
==================================================
ARIMA(3, 1, 4)
MAPE: 39.57%
MAE: 130679.12
==================================================
ARIMA(4, 0, 0)
MAPE: 35.08%
MAE: 113702.74
==================================================
ARIMA(4, 0, 1)
MAPE: 34.83%
MAE: 113468.08
==================================================
ARIMA(4, 0, 2)
MAPE: 37.49%
MAE: 125213.3
==================================================
ARIMA(4, 0, 3)
MAPE: 37.33%
MAE: 124997.09
==================================================
ARIMA(4, 0, 4)
MAPE: 24.55%
MAE: 83423.9
==================================================
ARIMA(4, 1, 0)
MAPE: 40.19%
MAE: 133910.13
==================================================
ARIMA(4, 1, 1)
MAPE: 42.53%
MAE: 140781.49
==================================================
ARIMA(4, 1, 2)
MAPE: 37.99%
MAE: 124991.37
==================================================
ARIMA(4, 1, 3)
MAPE: 34.46%
MAE: 114212.08
==================================================
ARIMA(4, 1, 4)
MAPE: 55.06%
MAE: 182197.07
==================================================

print(f"Best parameters model for BENGKULU:", best_param_bengkulu)
print(f"with the lowest MAPE: {lowest_mape_bengkulu.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_bengkulu.round(2)}")

Best parameters model for BENGKULU: (4, 0, 4)
with the lowest MAPE: 24.55%
with the lowest MAE: 83423.9

plot_predictions_vs_actual(prediction_series=prediction_bengkulu, actual_series=test_bengkulu)

train_lampung = training_data["LAMPUNG"]
test_lampung = test_data["LAMPUNG"]

best_param_lampung, lowest_mape_lampung, lowest_mae_lampung, prediction_lampung = make_arima_prediction(train_lampung, test_lampung)

all_provinces_predictions['LAMPUNG'] = prediction_lampung

ARIMA(0, 0, 0)
MAPE: 32.95%
MAE: 509844.9
==================================================
ARIMA(0, 0, 1)
MAPE: 29.31%
MAE: 449931.28
==================================================
ARIMA(0, 0, 2)
MAPE: 26.04%
MAE: 404506.25
==================================================
ARIMA(0, 0, 3)
MAPE: 21.64%
MAE: 340072.98
==================================================
ARIMA(0, 0, 4)
MAPE: 17.25%
MAE: 272934.41
==================================================
ARIMA(0, 1, 0)
MAPE: 10.66%
MAE: 153071.64
==================================================
ARIMA(0, 1, 1)
MAPE: 18.24%
MAE: 293590.75
==================================================
ARIMA(0, 1, 2)
MAPE: 19.87%
MAE: 317328.62
==================================================
ARIMA(0, 1, 3)
MAPE: 20.39%
MAE: 327173.74
==================================================
ARIMA(0, 1, 4)
MAPE: 20.5%
MAE: 329085.1
==================================================
ARIMA(1, 0, 0)
MAPE: 15.77%
MAE: 247959.83
==================================================
ARIMA(1, 0, 1)
MAPE: 12.17%
MAE: 191755.24
==================================================
ARIMA(1, 0, 2)
MAPE: 10.53%
MAE: 169207.69
==================================================
ARIMA(1, 0, 3)
MAPE: 10.42%
MAE: 166363.08
==================================================
ARIMA(1, 0, 4)
MAPE: 8.54%
MAE: 138459.95
==================================================
ARIMA(1, 1, 0)
MAPE: 13.83%
MAE: 217837.19
==================================================
ARIMA(1, 1, 1)
MAPE: 20.05%
MAE: 320414.31
==================================================
ARIMA(1, 1, 2)
MAPE: 26.26%
MAE: 415202.5
==================================================
ARIMA(1, 1, 3)
MAPE: 20.39%
MAE: 327238.01
==================================================
ARIMA(1, 1, 4)
MAPE: 19.78%
MAE: 319683.14
==================================================
ARIMA(2, 0, 0)
MAPE: 11.71%
MAE: 185857.36
==================================================
ARIMA(2, 0, 1)
MAPE: 11.83%
MAE: 187603.23
==================================================
ARIMA(2, 0, 2)
MAPE: 10.68%
MAE: 171074.34
==================================================
ARIMA(2, 0, 3)
MAPE: 9.96%
MAE: 160171.83
==================================================
ARIMA(2, 0, 4)
MAPE: 8.43%
MAE: 136799.21
==================================================
ARIMA(2, 1, 0)
MAPE: 14.1%
MAE: 225531.0
==================================================
ARIMA(2, 1, 1)
MAPE: 26.41%
MAE: 418080.45
==================================================
ARIMA(2, 1, 2)
MAPE: 27.08%
MAE: 428257.54
==================================================
ARIMA(2, 1, 3)
MAPE: 25.5%
MAE: 403811.78
==================================================
ARIMA(2, 1, 4)
MAPE: 19.63%
MAE: 316848.74
==================================================
ARIMA(3, 0, 0)
MAPE: 11.78%
MAE: 186884.13
==================================================
ARIMA(3, 0, 1)
MAPE: 11.8%
MAE: 187633.86
==================================================
ARIMA(3, 0, 2)
MAPE: 10.5%
MAE: 169441.06
==================================================
ARIMA(3, 0, 3)
MAPE: 10.99%
MAE: 173642.38
==================================================
ARIMA(3, 0, 4)
MAPE: 8.73%
MAE: 137343.94
==================================================
ARIMA(3, 1, 0)
MAPE: 14.66%
MAE: 238549.35
==================================================
ARIMA(3, 1, 1)
MAPE: 19.6%
MAE: 314755.57
==================================================
ARIMA(3, 1, 2)
MAPE: 19.43%
MAE: 312391.87
==================================================
ARIMA(3, 1, 3)
MAPE: 26.06%
MAE: 413526.08
==================================================
ARIMA(3, 1, 4)
MAPE: 26.22%
MAE: 415725.52
==================================================
ARIMA(4, 0, 0)
MAPE: 11.4%
MAE: 182497.2
==================================================
ARIMA(4, 0, 1)
MAPE: 11.39%
MAE: 182467.92
==================================================
ARIMA(4, 0, 2)
MAPE: 10.52%
MAE: 166593.65
==================================================
ARIMA(4, 0, 3)
MAPE: 9.87%
MAE: 157493.45
==================================================
ARIMA(4, 0, 4)
MAPE: 9.09%
MAE: 143889.73
==================================================
ARIMA(4, 1, 0)
MAPE: 19.52%
MAE: 313966.02
==================================================
ARIMA(4, 1, 1)
MAPE: 18.39%
MAE: 297545.87
==================================================
ARIMA(4, 1, 2)
MAPE: 18.62%
MAE: 300854.72
==================================================
ARIMA(4, 1, 3)
MAPE: 24.98%
MAE: 396910.44
==================================================
ARIMA(4, 1, 4)
MAPE: 23.23%
MAE: 370247.61
==================================================

print(f"Best parameters model for LAMPUNG:", best_param_lampung)
print(f"with the lowest MAPE: {lowest_mape_lampung.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_lampung.round(2)}")

Best parameters model for LAMPUNG: (2, 0, 4)
with the lowest MAPE: 8.43%
with the lowest MAE: 136799.21

plot_predictions_vs_actual(prediction_series=prediction_lampung, actual_series=test_lampung)

train_kep_bangka_belitung = training_data["KEP. BANGKA BELITUNG"]
test_kep_bangka_belitung = test_data["KEP. BANGKA BELITUNG"]

best_param_kep_bangka_belitung, lowest_mape_kep_bangka_belitung, lowest_mae_kep_bangka_belitung, prediction_kep_bangka_belitung = make_arima_prediction(train_kep_bangka_belitung, test_kep_bangka_belitung)

all_provinces_predictions['KEP. BANGKA BELITUNG'] = prediction_kep_bangka_belitung

ARIMA(0, 0, 0)
MAPE: 41.76%
MAE: 104457.25
==================================================
ARIMA(0, 0, 1)
MAPE: 37.51%
MAE: 93806.21
==================================================
ARIMA(0, 0, 2)
MAPE: 34.98%
MAE: 87499.27
==================================================
ARIMA(0, 0, 3)
MAPE: 27.72%
MAE: 70045.32
==================================================
ARIMA(0, 0, 4)
MAPE: 32.43%
MAE: 78354.06
==================================================
ARIMA(0, 1, 0)
MAPE: 62.56%
MAE: 151469.01
==================================================
ARIMA(0, 1, 1)
MAPE: 31.19%
MAE: 74478.23
==================================================
ARIMA(0, 1, 2)
MAPE: 21.67%
MAE: 51520.62
==================================================
ARIMA(0, 1, 3)
MAPE: 24.01%
MAE: 57635.07
==================================================
ARIMA(0, 1, 4)
MAPE: 27.58%
MAE: 66081.03
==================================================
ARIMA(1, 0, 0)
MAPE: 30.92%
MAE: 74126.02
==================================================
ARIMA(1, 0, 1)
MAPE: 22.47%
MAE: 53390.07
==================================================
ARIMA(1, 0, 2)
MAPE: 28.55%
MAE: 68058.06
==================================================
ARIMA(1, 0, 3)
MAPE: 23.16%
MAE: 54970.5
==================================================
ARIMA(1, 0, 4)
MAPE: 25.69%
MAE: 60560.11
==================================================
ARIMA(1, 1, 0)
MAPE: 39.25%
MAE: 94212.66
==================================================
ARIMA(1, 1, 1)
MAPE: 40.44%
MAE: 97139.09
==================================================
ARIMA(1, 1, 2)
MAPE: 20.98%
MAE: 50338.47
==================================================
ARIMA(1, 1, 3)
MAPE: 25.58%
MAE: 61306.86
==================================================
ARIMA(1, 1, 4)
MAPE: 31.37%
MAE: 75747.11
==================================================
ARIMA(2, 0, 0)
MAPE: 22.94%
MAE: 54488.31
==================================================
ARIMA(2, 0, 1)
MAPE: 23.47%
MAE: 55863.26
==================================================
ARIMA(2, 0, 2)
MAPE: 26.95%
MAE: 64187.32
==================================================
ARIMA(2, 0, 3)
MAPE: 23.78%
MAE: 56253.19
==================================================
ARIMA(2, 0, 4)
MAPE: 33.48%
MAE: 78769.25
==================================================
ARIMA(2, 1, 0)
MAPE: 39.74%
MAE: 95409.86
==================================================
ARIMA(2, 1, 1)
MAPE: 21.1%
MAE: 50472.65
==================================================
ARIMA(2, 1, 2)
MAPE: 21.38%
MAE: 51096.63
==================================================
ARIMA(2, 1, 3)
MAPE: 26.32%
MAE: 63731.91
==================================================
ARIMA(2, 1, 4)
MAPE: 30.91%
MAE: 74841.15
==================================================
ARIMA(3, 0, 0)
MAPE: 24.16%
MAE: 57562.59
==================================================
ARIMA(3, 0, 1)
MAPE: 24.19%
MAE: 57622.93
==================================================
ARIMA(3, 0, 2)
MAPE: 31.43%
MAE: 74624.08
==================================================
ARIMA(3, 0, 3)
MAPE: 27.04%
MAE: 64344.07
==================================================
ARIMA(3, 0, 4)
MAPE: 28.0%
MAE: 66752.51
==================================================
ARIMA(3, 1, 0)
MAPE: 34.36%
MAE: 82413.29
==================================================
ARIMA(3, 1, 1)
MAPE: 22.28%
MAE: 53863.07
==================================================
ARIMA(3, 1, 2)
MAPE: 23.7%
MAE: 56581.38
==================================================
ARIMA(3, 1, 3)
MAPE: 24.2%
MAE: 58834.84
==================================================
ARIMA(3, 1, 4)
MAPE: 30.87%
MAE: 75059.42
==================================================
ARIMA(4, 0, 0)
MAPE: 24.76%
MAE: 58813.38
==================================================
ARIMA(4, 0, 1)
MAPE: 25.35%
MAE: 59985.45
==================================================
ARIMA(4, 0, 2)
MAPE: 28.0%
MAE: 66451.74
==================================================
ARIMA(4, 0, 3)
MAPE: 26.66%
MAE: 63830.19
==================================================
ARIMA(4, 0, 4)
MAPE: 26.69%
MAE: 63510.16
==================================================
ARIMA(4, 1, 0)
MAPE: 27.7%
MAE: 67585.63
==================================================
ARIMA(4, 1, 1)
MAPE: 28.13%
MAE: 68590.58
==================================================
ARIMA(4, 1, 2)
MAPE: 25.94%
MAE: 63514.88
==================================================
ARIMA(4, 1, 3)
MAPE: 27.13%
MAE: 66047.17
==================================================
ARIMA(4, 1, 4)
MAPE: 29.16%
MAE: 69514.84
==================================================

print(f"Best parameters model for KEP. BANGKA BELITUNG:", best_param_kep_bangka_belitung)
print(f"with the lowest MAPE: {lowest_mape_kep_bangka_belitung.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_kep_bangka_belitung.round(2)}")

Best parameters model for KEP. BANGKA BELITUNG: (1, 1, 2)
with the lowest MAPE: 20.98%
with the lowest MAE: 50338.47

plot_predictions_vs_actual(prediction_series=prediction_kep_bangka_belitung, actual_series=test_kep_bangka_belitung)

train_kep_riau = training_data["KEP. RIAU"]
test_kep_riau = test_data["KEP. RIAU"]

best_param_kep_riau, lowest_mape_kep_riau, lowest_mae_kep_riau, prediction_kep_riau = make_arima_prediction(train_kep_riau, test_kep_riau)

all_provinces_predictions['KEP. RIAU'] = prediction_kep_riau

ARIMA(0, 0, 0)
MAPE: 44.38%
MAE: 126939.1
==================================================
ARIMA(0, 0, 1)
MAPE: 37.92%
MAE: 108630.4
==================================================
ARIMA(0, 0, 2)
MAPE: 34.67%
MAE: 99564.26
==================================================
ARIMA(0, 0, 3)
MAPE: 27.0%
MAE: 78376.86
==================================================
ARIMA(0, 0, 4)
MAPE: 24.73%
MAE: 71582.92
==================================================
ARIMA(0, 1, 0)
MAPE: 6.31%
MAE: 17842.21
==================================================
ARIMA(0, 1, 1)
MAPE: 9.03%
MAE: 27465.3
==================================================
ARIMA(0, 1, 2)
MAPE: 11.03%
MAE: 32998.12
==================================================
ARIMA(0, 1, 3)
MAPE: 11.78%
MAE: 35010.6
==================================================
ARIMA(0, 1, 4)
MAPE: 11.79%
MAE: 35021.46
==================================================
ARIMA(1, 0, 0)
MAPE: 11.45%
MAE: 34157.42
==================================================
ARIMA(1, 0, 1)
MAPE: 13.15%
MAE: 39104.48
==================================================
ARIMA(1, 0, 2)
MAPE: 13.36%
MAE: 39653.56
==================================================
ARIMA(1, 0, 3)
MAPE: 13.39%
MAE: 39557.76
==================================================
ARIMA(1, 0, 4)
MAPE: 12.3%
MAE: 35904.68
==================================================
ARIMA(1, 1, 0)
MAPE: 7.43%
MAE: 22509.18
==================================================
ARIMA(1, 1, 1)
MAPE: 11.23%
MAE: 33551.98
==================================================
ARIMA(1, 1, 2)
MAPE: 11.28%
MAE: 33682.45
==================================================
ARIMA(1, 1, 3)
MAPE: 11.78%
MAE: 35012.31
==================================================
ARIMA(1, 1, 4)
MAPE: 11.48%
MAE: 34404.71
==================================================
ARIMA(2, 0, 0)
MAPE: 12.93%
MAE: 38430.24
==================================================
ARIMA(2, 0, 1)
MAPE: 13.44%
MAE: 39962.41
==================================================
ARIMA(2, 0, 2)
MAPE: 13.15%
MAE: 39015.38
==================================================
ARIMA(2, 0, 3)
MAPE: 15.46%
MAE: 46252.86
==================================================
ARIMA(2, 0, 4)
MAPE: 12.42%
MAE: 36478.13
==================================================
ARIMA(2, 1, 0)
MAPE: 8.58%
MAE: 26129.07
==================================================
ARIMA(2, 1, 1)
MAPE: 11.23%
MAE: 33560.79
==================================================
ARIMA(2, 1, 2)
MAPE: 11.07%
MAE: 32184.28
==================================================
ARIMA(2, 1, 3)
MAPE: 10.75%
MAE: 32633.3
==================================================
ARIMA(2, 1, 4)
MAPE: 13.69%
MAE: 40837.45
==================================================
ARIMA(3, 0, 0)
MAPE: 13.0%
MAE: 38651.86
==================================================
ARIMA(3, 0, 1)
MAPE: 12.95%
MAE: 38566.79
==================================================
ARIMA(3, 0, 2)
MAPE: 12.93%
MAE: 38816.92
==================================================
ARIMA(3, 0, 3)
MAPE: 12.28%
MAE: 36820.61
==================================================
ARIMA(3, 0, 4)
MAPE: 13.83%
MAE: 40715.58
==================================================
ARIMA(3, 1, 0)
MAPE: 10.84%
MAE: 32449.14
==================================================
ARIMA(3, 1, 1)
MAPE: 12.25%
MAE: 36416.02
==================================================
ARIMA(3, 1, 2)
MAPE: 13.97%
MAE: 41650.8
==================================================
ARIMA(3, 1, 3)
MAPE: 10.87%
MAE: 33046.13
==================================================
ARIMA(3, 1, 4)
MAPE: 10.37%
MAE: 31193.57
==================================================
ARIMA(4, 0, 0)
MAPE: 13.5%
MAE: 40078.47
==================================================
ARIMA(4, 0, 1)
MAPE: 12.89%
MAE: 38406.6
==================================================
ARIMA(4, 0, 2)
MAPE: 12.5%
MAE: 37582.67
==================================================
ARIMA(4, 0, 3)
MAPE: 12.4%
MAE: 37168.1
==================================================
ARIMA(4, 0, 4)
MAPE: 18.55%
MAE: 53256.55
==================================================
ARIMA(4, 1, 0)
MAPE: 14.79%
MAE: 43722.5
==================================================
ARIMA(4, 1, 1)
MAPE: 14.37%
MAE: 42614.14
==================================================
ARIMA(4, 1, 2)
MAPE: 11.32%
MAE: 34280.23
==================================================
ARIMA(4, 1, 3)
MAPE: 11.12%
MAE: 33699.02
==================================================
ARIMA(4, 1, 4)
MAPE: 14.42%
MAE: 42672.89
==================================================

print(f"Best parameters model for KEP. RIAU:", best_param_kep_riau)
print(f"with the lowest MAPE: {lowest_mape_kep_riau.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_kep_riau.round(2)}")

Best parameters model for KEP. RIAU: (0, 1, 0)
with the lowest MAPE: 6.31%
with the lowest MAE: 17842.21

plot_predictions_vs_actual(prediction_series=prediction_kep_riau, actual_series=test_kep_riau)

train_dki_jakarta = training_data["DKI JAKARTA"]    
test_dki_jakarta = test_data["DKI JAKARTA"]

best_param_dki_jakarta, lowest_mape_dki_jakarta, lowest_mae_dki_jakarta, prediction_dki_jakarta = make_arima_prediction(train_dki_jakarta, test_dki_jakarta)

all_provinces_predictions['DKI JAKARTA'] = prediction_dki_jakarta

ARIMA(0, 0, 0)
MAPE: 36.12%
MAE: 2399869.48
==================================================
ARIMA(0, 0, 1)
MAPE: 31.04%
MAE: 2066425.05
==================================================
ARIMA(0, 0, 2)
MAPE: 28.26%
MAE: 1883664.21
==================================================
ARIMA(0, 0, 3)
MAPE: 24.76%
MAE: 1654287.31
==================================================
ARIMA(0, 0, 4)
MAPE: 23.17%
MAE: 1548727.42
==================================================
ARIMA(0, 1, 0)
MAPE: 13.15%
MAE: 865895.26
==================================================
ARIMA(0, 1, 1)
MAPE: 11.08%
MAE: 729089.45
==================================================
ARIMA(0, 1, 2)
MAPE: 10.09%
MAE: 663060.73
==================================================
ARIMA(0, 1, 3)
MAPE: 11.02%
MAE: 724984.43
==================================================
ARIMA(0, 1, 4)
MAPE: 9.33%
MAE: 612407.18
==================================================
ARIMA(1, 0, 0)
MAPE: 10.23%
MAE: 685770.0
==================================================
ARIMA(1, 0, 1)
MAPE: 13.46%
MAE: 900375.46
==================================================
ARIMA(1, 0, 2)
MAPE: 12.0%
MAE: 803415.75
==================================================
ARIMA(1, 0, 3)
MAPE: 11.46%
MAE: 767443.63
==================================================
ARIMA(1, 0, 4)
MAPE: 11.32%
MAE: 757593.78
==================================================
ARIMA(1, 1, 0)
MAPE: 11.4%
MAE: 750044.34
==================================================
ARIMA(1, 1, 1)
MAPE: 12.61%
MAE: 830045.34
==================================================
ARIMA(1, 1, 2)
MAPE: 9.33%
MAE: 612824.51
==================================================
ARIMA(1, 1, 3)
MAPE: 9.46%
MAE: 621638.13
==================================================
ARIMA(1, 1, 4)
MAPE: 9.17%
MAE: 602460.11
==================================================
ARIMA(2, 0, 0)
MAPE: 12.84%
MAE: 858779.46
==================================================
ARIMA(2, 0, 1)
MAPE: 11.14%
MAE: 746925.98
==================================================
ARIMA(2, 0, 2)
MAPE: 12.61%
MAE: 844144.17
==================================================
ARIMA(2, 0, 3)
MAPE: 11.11%
MAE: 743618.44
==================================================
ARIMA(2, 0, 4)
MAPE: 10.98%
MAE: 734501.49
==================================================
ARIMA(2, 1, 0)
MAPE: 10.19%
MAE: 669985.65
==================================================
ARIMA(2, 1, 1)
MAPE: 9.58%
MAE: 629249.47
==================================================
ARIMA(2, 1, 2)
MAPE: 13.5%
MAE: 890208.2
==================================================
ARIMA(2, 1, 3)
MAPE: 4.79%
MAE: 318142.9
==================================================
ARIMA(2, 1, 4)
MAPE: 4.38%
MAE: 293137.86
==================================================
ARIMA(3, 0, 0)
MAPE: 11.91%
MAE: 797121.05
==================================================
ARIMA(3, 0, 1)
MAPE: 12.43%
MAE: 832167.35
==================================================
ARIMA(3, 0, 2)
MAPE: 15.69%
MAE: 1050002.32
==================================================
ARIMA(3, 0, 3)
MAPE: 10.57%
MAE: 704432.15
==================================================
ARIMA(3, 0, 4)
MAPE: 11.64%
MAE: 779607.26
==================================================
ARIMA(3, 1, 0)
MAPE: 10.98%
MAE: 722209.5
==================================================
ARIMA(3, 1, 1)
MAPE: 9.48%
MAE: 622737.86
==================================================
ARIMA(3, 1, 2)
MAPE: 4.78%
MAE: 317301.36
==================================================
ARIMA(3, 1, 3)
MAPE: 4.66%
MAE: 309652.83
==================================================
ARIMA(3, 1, 4)
MAPE: 5.17%
MAE: 343752.8
==================================================
ARIMA(4, 0, 0)
MAPE: 11.58%
MAE: 774908.08
==================================================
ARIMA(4, 0, 1)
MAPE: 11.4%
MAE: 762531.0
==================================================
ARIMA(4, 0, 2)
MAPE: 11.41%
MAE: 763403.95
==================================================
ARIMA(4, 0, 3)
MAPE: 15.96%
MAE: 1066540.42
==================================================
ARIMA(4, 0, 4)
MAPE: 11.68%
MAE: 782654.69
==================================================
ARIMA(4, 1, 0)
MAPE: 9.74%
MAE: 640154.21
==================================================
ARIMA(4, 1, 1)
MAPE: 9.3%
MAE: 610696.79
==================================================
ARIMA(4, 1, 2)
MAPE: 4.72%
MAE: 313878.09
==================================================
ARIMA(4, 1, 3)
MAPE: 9.45%
MAE: 620643.99
==================================================
ARIMA(4, 1, 4)
MAPE: 13.14%
MAE: 866094.21
==================================================

print(f"Best parameters model for DKI JAKARTA:", best_param_dki_jakarta)
print(f"with the lowest MAPE: {lowest_mape_dki_jakarta.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_dki_jakarta.round(2)}")

Best parameters model for DKI JAKARTA: (2, 1, 4)
with the lowest MAPE: 4.38%
with the lowest MAE: 293137.86

plot_predictions_vs_actual(prediction_series=prediction_dki_jakarta, actual_series=test_dki_jakarta)

train_jawa_barat = training_data["JAWA BARAT"]
test_jawa_barat = test_data["JAWA BARAT"]

best_param_jawa_barat, lowest_mape_jawa_barat, lowest_mae_jawa_barat, prediction_jawa_barat = make_arima_prediction(train_jawa_barat, test_jawa_barat)

all_provinces_predictions['JAWA BARAT'] = prediction_jawa_barat

ARIMA(0, 0, 0)
MAPE: 23.6%
MAE: 3393660.13
==================================================
ARIMA(0, 0, 1)
MAPE: 19.38%
MAE: 2805755.7
==================================================
ARIMA(0, 0, 2)
MAPE: 16.53%
MAE: 2434434.0
==================================================
ARIMA(0, 0, 3)
MAPE: 15.73%
MAE: 2343647.91
==================================================
ARIMA(0, 0, 4)
MAPE: 15.47%
MAE: 2302806.32
==================================================
ARIMA(0, 1, 0)
MAPE: 25.93%
MAE: 3377274.53
==================================================
ARIMA(0, 1, 1)
MAPE: 17.53%
MAE: 2334559.96
==================================================
ARIMA(0, 1, 2)
MAPE: 18.91%
MAE: 2500380.09
==================================================
ARIMA(0, 1, 3)
MAPE: 15.03%
MAE: 2044894.11
==================================================
ARIMA(0, 1, 4)
MAPE: 14.76%
MAE: 2014915.96
==================================================
ARIMA(1, 0, 0)
MAPE: 13.01%
MAE: 1940535.79
==================================================
ARIMA(1, 0, 1)
MAPE: 10.68%
MAE: 1575721.88
==================================================
ARIMA(1, 0, 2)
MAPE: 14.23%
MAE: 2092775.51
==================================================
ARIMA(1, 0, 3)
MAPE: 11.05%
MAE: 1632694.44
==================================================
ARIMA(1, 0, 4)
MAPE: 11.46%
MAE: 1687277.12
==================================================
ARIMA(1, 1, 0)
MAPE: 19.25%
MAE: 2540488.47
==================================================
ARIMA(1, 1, 1)
MAPE: 19.73%
MAE: 2598614.59
==================================================
ARIMA(1, 1, 2)
MAPE: 19.66%
MAE: 2590104.41
==================================================
ARIMA(1, 1, 3)
MAPE: 14.97%
MAE: 2038684.89
==================================================
ARIMA(1, 1, 4)
MAPE: 13.22%
MAE: 1821549.27
==================================================
ARIMA(2, 0, 0)
MAPE: 10.94%
MAE: 1606904.81
==================================================
ARIMA(2, 0, 1)
MAPE: 11.41%
MAE: 1674778.11
==================================================
ARIMA(2, 0, 2)
MAPE: 13.23%
MAE: 1941834.99
==================================================
ARIMA(2, 0, 3)
MAPE: 11.26%
MAE: 1661738.54
==================================================
ARIMA(2, 0, 4)
MAPE: 11.58%
MAE: 1710345.91
==================================================
ARIMA(2, 1, 0)
MAPE: 19.72%
MAE: 2597951.73
==================================================
ARIMA(2, 1, 1)
MAPE: 19.72%
MAE: 2597633.07
==================================================
ARIMA(2, 1, 2)
MAPE: 11.84%
MAE: 1645706.29
==================================================
ARIMA(2, 1, 3)
MAPE: 18.22%
MAE: 2452228.14
==================================================
ARIMA(2, 1, 4)
MAPE: 15.05%
MAE: 2051404.24
==================================================
ARIMA(3, 0, 0)
MAPE: 11.97%
MAE: 1754615.05
==================================================
ARIMA(3, 0, 1)
MAPE: 11.92%
MAE: 1748081.69
==================================================
ARIMA(3, 0, 2)
MAPE: 14.32%
MAE: 2121161.53
==================================================
ARIMA(3, 0, 3)
MAPE: 10.78%
MAE: 1603954.33
==================================================
ARIMA(3, 0, 4)
MAPE: 10.75%
MAE: 1598456.55
==================================================
ARIMA(3, 1, 0)
MAPE: 18.82%
MAE: 2489027.19
==================================================
ARIMA(3, 1, 1)
MAPE: 12.03%
MAE: 1670271.97
==================================================
ARIMA(3, 1, 2)
MAPE: 20.64%
MAE: 2722078.52
==================================================
ARIMA(3, 1, 3)
MAPE: 12.28%
MAE: 1736340.56
==================================================
ARIMA(3, 1, 4)
MAPE: 12.02%
MAE: 1693290.21
==================================================
ARIMA(4, 0, 0)
MAPE: 11.69%
MAE: 1715085.1
==================================================
ARIMA(4, 0, 1)
MAPE: 12.37%
MAE: 1814242.53
==================================================
ARIMA(4, 0, 2)
MAPE: 11.72%
MAE: 1731619.72
==================================================
ARIMA(4, 0, 3)
MAPE: 10.74%
MAE: 1597751.57
==================================================
ARIMA(4, 0, 4)
MAPE: 11.02%
MAE: 1632545.51
==================================================
ARIMA(4, 1, 0)
MAPE: 15.36%
MAE: 2098474.53
==================================================
ARIMA(4, 1, 1)
MAPE: 14.87%
MAE: 2023779.82
==================================================
ARIMA(4, 1, 2)
MAPE: 15.16%
MAE: 2058138.4
==================================================
ARIMA(4, 1, 3)
MAPE: 18.71%
MAE: 2499548.11
==================================================
ARIMA(4, 1, 4)
MAPE: 19.25%
MAE: 2565195.82
==================================================

print(f"Best parameters model for JAWA BARAT:", best_param_jawa_barat)
print(f"with the lowest MAPE: {lowest_mape_jawa_barat.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_jawa_barat.round(2)}")

Best parameters model for JAWA BARAT: (1, 0, 1)
with the lowest MAPE: 10.68%
with the lowest MAE: 1575721.88

plot_predictions_vs_actual(prediction_series=prediction_jawa_barat, actual_series=test_jawa_barat)

train_jawa_tengah = training_data["JAWA TENGAH"]
test_jawa_tengah = test_data["JAWA TENGAH"]

best_param_jawa_tengah, lowest_mape_jawa_tengah, lowest_mae_jawa_tengah, prediction_jawa_tengah = make_arima_prediction(train_jawa_tengah, test_jawa_tengah)

all_provinces_predictions['JAWA TENGAH'] = prediction_jawa_tengah

ARIMA(0, 0, 0)
MAPE: 23.31%
MAE: 3369341.72
==================================================
ARIMA(0, 0, 1)
MAPE: 23.17%
MAE: 3348095.82
==================================================
ARIMA(0, 0, 2)
MAPE: 24.4%
MAE: 3521315.36
==================================================
ARIMA(0, 0, 3)
MAPE: 25.94%
MAE: 3706736.09
==================================================
ARIMA(0, 0, 4)
MAPE: 25.91%
MAE: 3702765.43
==================================================
ARIMA(0, 1, 0)
MAPE: 27.42%
MAE: 3880429.29
==================================================
ARIMA(0, 1, 1)
MAPE: 30.05%
MAE: 4223575.94
==================================================
ARIMA(0, 1, 2)
MAPE: 30.12%
MAE: 4242198.29
==================================================
ARIMA(0, 1, 3)
MAPE: 32.43%
MAE: 4544492.53
==================================================
ARIMA(0, 1, 4)
MAPE: 29.52%
MAE: 4160307.26
==================================================
ARIMA(1, 0, 0)
MAPE: 24.76%
MAE: 3555139.35
==================================================
ARIMA(1, 0, 1)
MAPE: 26.26%
MAE: 3751808.04
==================================================
ARIMA(1, 0, 2)
MAPE: 27.92%
MAE: 3970636.03
==================================================
ARIMA(1, 0, 3)
MAPE: 25.9%
MAE: 3702041.95
==================================================
ARIMA(1, 0, 4)
MAPE: 25.82%
MAE: 3691103.86
==================================================
ARIMA(1, 1, 0)
MAPE: 30.88%
MAE: 4335988.97
==================================================
ARIMA(1, 1, 1)
MAPE: 30.85%
MAE: 4333073.4
==================================================
ARIMA(1, 1, 2)
MAPE: 31.17%
MAE: 4380668.74
==================================================
ARIMA(1, 1, 3)
MAPE: 31.38%
MAE: 4407510.77
==================================================
ARIMA(1, 1, 4)
MAPE: 30.26%
MAE: 4253631.41
==================================================
ARIMA(2, 0, 0)
MAPE: 27.11%
MAE: 3866090.83
==================================================
ARIMA(2, 0, 1)
MAPE: 26.89%
MAE: 3839482.75
==================================================
ARIMA(2, 0, 2)
MAPE: 25.2%
MAE: 3612271.81
==================================================
ARIMA(2, 0, 3)
MAPE: 25.32%
MAE: 3624499.2
==================================================
ARIMA(2, 0, 4)
MAPE: 25.93%
MAE: 3706023.76
==================================================
ARIMA(2, 1, 0)
MAPE: 30.83%
MAE: 4331175.34
==================================================
ARIMA(2, 1, 1)
MAPE: 30.84%
MAE: 4332693.39
==================================================
ARIMA(2, 1, 2)
MAPE: 28.59%
MAE: 4043227.35
==================================================
ARIMA(2, 1, 3)
MAPE: 30.97%
MAE: 4351210.09
==================================================
ARIMA(2, 1, 4)
MAPE: 29.93%
MAE: 4211118.77
==================================================
ARIMA(3, 0, 0)
MAPE: 25.62%
MAE: 3678287.0
==================================================
ARIMA(3, 0, 1)
MAPE: 24.62%
MAE: 3553174.11
==================================================
ARIMA(3, 0, 2)
MAPE: 25.99%
MAE: 3712920.33
==================================================
ARIMA(3, 0, 3)
MAPE: 26.54%
MAE: 3786711.44
==================================================
ARIMA(3, 0, 4)
MAPE: 24.82%
MAE: 3564429.3
==================================================
ARIMA(3, 1, 0)
MAPE: 30.94%
MAE: 4347488.62
==================================================
ARIMA(3, 1, 1)
MAPE: 32.81%
MAE: 4599189.88
==================================================
ARIMA(3, 1, 2)
MAPE: 25.88%
MAE: 3698706.68
==================================================
ARIMA(3, 1, 3)
MAPE: 32.52%
MAE: 4570478.06
==================================================
ARIMA(3, 1, 4)
MAPE: 30.95%
MAE: 4349650.06
==================================================
ARIMA(4, 0, 0)
MAPE: 24.15%
MAE: 3496068.7
==================================================
ARIMA(4, 0, 1)
MAPE: 25.46%
MAE: 3673885.74
==================================================
ARIMA(4, 0, 2)
MAPE: 25.71%
MAE: 3706092.99
==================================================
ARIMA(4, 0, 3)
MAPE: 26.2%
MAE: 3742608.88
==================================================
ARIMA(4, 0, 4)
MAPE: 26.99%
MAE: 3847458.9
==================================================
ARIMA(4, 1, 0)
MAPE: 33.01%
MAE: 4621901.9
==================================================
ARIMA(4, 1, 1)
MAPE: 27.79%
MAE: 3943261.83
==================================================
ARIMA(4, 1, 2)
MAPE: 29.4%
MAE: 4162975.57
==================================================
ARIMA(4, 1, 3)
MAPE: 29.27%
MAE: 4145569.72
==================================================
ARIMA(4, 1, 4)
MAPE: 30.09%
MAE: 4235776.54
==================================================

print(f"Best parameters model for JAWA TENGAH:", best_param_jawa_tengah)
print(f"with the lowest MAPE: {lowest_mape_jawa_tengah.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_jawa_tengah.round(2)}")

Best parameters model for JAWA TENGAH: (0, 0, 1)
with the lowest MAPE: 23.17%
with the lowest MAE: 3348095.82

plot_predictions_vs_actual(prediction_series=prediction_jawa_tengah, actual_series=test_jawa_tengah)

train_di_yogyakarta = training_data["DI YOGYAKARTA"]
test_di_yogyakarta = test_data["DI YOGYAKARTA"]

best_param_di_yogyakarta, lowest_mape_di_yogyakarta, lowest_mae_di_yogyakarta, prediction_di_yogyakarta = make_arima_prediction(train_di_yogyakarta, test_di_yogyakarta)

all_provinces_predictions['DI YOGYAKARTA'] = prediction_di_yogyakarta

ARIMA(0, 0, 0)
MAPE: 35.27%
MAE: 1229887.2
==================================================
ARIMA(0, 0, 1)
MAPE: 34.29%
MAE: 1188821.37
==================================================
ARIMA(0, 0, 2)
MAPE: 33.28%
MAE: 1156399.19
==================================================
ARIMA(0, 0, 3)
MAPE: 32.56%
MAE: 1134794.51
==================================================
ARIMA(0, 0, 4)
MAPE: 32.65%
MAE: 1137724.55
==================================================
ARIMA(0, 1, 0)
MAPE: 17.35%
MAE: 634815.63
==================================================
ARIMA(0, 1, 1)
MAPE: 19.3%
MAE: 706963.97
==================================================
ARIMA(0, 1, 2)
MAPE: 20.28%
MAE: 738446.61
==================================================
ARIMA(0, 1, 3)
MAPE: 22.94%
MAE: 825787.63
==================================================
ARIMA(0, 1, 4)
MAPE: 23.15%
MAE: 830624.7
==================================================
ARIMA(1, 0, 0)
MAPE: 31.61%
MAE: 1103406.91
==================================================
ARIMA(1, 0, 1)
MAPE: 31.36%
MAE: 1095915.78
==================================================
ARIMA(1, 0, 2)
MAPE: 32.1%
MAE: 1119659.1
==================================================
ARIMA(1, 0, 3)
MAPE: 32.78%
MAE: 1142162.34
==================================================
ARIMA(1, 0, 4)
MAPE: 32.65%
MAE: 1137501.88
==================================================
ARIMA(1, 1, 0)
MAPE: 18.85%
MAE: 691182.72
==================================================
ARIMA(1, 1, 1)
MAPE: 22.47%
MAE: 807646.82
==================================================
ARIMA(1, 1, 2)
MAPE: 22.59%
MAE: 812005.0
==================================================
ARIMA(1, 1, 3)
MAPE: 22.99%
MAE: 826328.95
==================================================
ARIMA(1, 1, 4)
MAPE: 24.16%
MAE: 866997.57
==================================================
ARIMA(2, 0, 0)
MAPE: 31.26%
MAE: 1093211.23
==================================================
ARIMA(2, 0, 1)
MAPE: 31.2%
MAE: 1091477.65
==================================================
ARIMA(2, 0, 2)
MAPE: 32.97%
MAE: 1146088.93
==================================================
ARIMA(2, 0, 3)
MAPE: 33.45%
MAE: 1159753.78
==================================================
ARIMA(2, 0, 4)
MAPE: 29.37%
MAE: 1034562.41
==================================================
ARIMA(2, 1, 0)
MAPE: 18.72%
MAE: 686269.32
==================================================
ARIMA(2, 1, 1)
MAPE: 18.67%
MAE: 684580.67
==================================================
ARIMA(2, 1, 2)
MAPE: 22.66%
MAE: 815014.24
==================================================
ARIMA(2, 1, 3)
MAPE: 23.07%
MAE: 826034.24
==================================================
ARIMA(2, 1, 4)
MAPE: 24.1%
MAE: 863664.95
==================================================
ARIMA(3, 0, 0)
MAPE: 32.25%
MAE: 1124162.91
==================================================
ARIMA(3, 0, 1)
MAPE: 32.22%
MAE: 1123227.47
==================================================
ARIMA(3, 0, 2)
MAPE: 32.69%
MAE: 1133774.77
==================================================
ARIMA(3, 0, 3)
MAPE: 32.34%
MAE: 1124635.26
==================================================
ARIMA(3, 0, 4)
MAPE: 29.6%
MAE: 1041530.14
==================================================
ARIMA(3, 1, 0)
MAPE: 19.32%
MAE: 706915.91
==================================================
ARIMA(3, 1, 1)
MAPE: 21.8%
MAE: 786334.02
==================================================
ARIMA(3, 1, 2)
MAPE: 21.13%
MAE: 765021.71
==================================================
ARIMA(3, 1, 3)
MAPE: 23.17%
MAE: 829253.47
==================================================
ARIMA(3, 1, 4)
MAPE: 23.85%
MAE: 854197.19
==================================================
ARIMA(4, 0, 0)
MAPE: 32.11%
MAE: 1119590.78
==================================================
ARIMA(4, 0, 1)
MAPE: 33.11%
MAE: 1153926.39
==================================================
ARIMA(4, 0, 2)
MAPE: 32.82%
MAE: 1134452.4
==================================================
ARIMA(4, 0, 3)
MAPE: 32.47%
MAE: 1130027.06
==================================================
ARIMA(4, 0, 4)
MAPE: 29.25%
MAE: 1025448.59
==================================================
ARIMA(4, 1, 0)
MAPE: 22.86%
MAE: 822461.4
==================================================
ARIMA(4, 1, 1)
MAPE: 20.87%
MAE: 756480.34
==================================================
ARIMA(4, 1, 2)
MAPE: 22.12%
MAE: 799636.24
==================================================
ARIMA(4, 1, 3)
MAPE: 22.36%
MAE: 806794.26
==================================================
ARIMA(4, 1, 4)
MAPE: 23.61%
MAE: 845901.93
==================================================

print(f"Best parameters model for DI YOGYAKARTA:", best_param_di_yogyakarta)
print(f"with the lowest MAPE: {lowest_mape_di_yogyakarta.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_di_yogyakarta.round(2)}")

Best parameters model for DI YOGYAKARTA: (0, 1, 0)
with the lowest MAPE: 17.35%
with the lowest MAE: 634815.63

plot_predictions_vs_actual(prediction_series=prediction_di_yogyakarta, actual_series=test_di_yogyakarta)

train_jawa_timur = training_data["JAWA TIMUR"]
test_jawa_timur = test_data["JAWA TIMUR"]

best_param_jawa_timur, lowest_mape_jawa_timur, lowest_mae_jawa_timur, prediction_jawa_timur = make_arima_prediction(train_jawa_timur, test_jawa_timur)

all_provinces_predictions['JAWA TIMUR'] = prediction_jawa_timur

ARIMA(0, 0, 0)
MAPE: 14.17%
MAE: 3014299.1
==================================================
ARIMA(0, 0, 1)
MAPE: 19.39%
MAE: 4043439.7
==================================================
ARIMA(0, 0, 2)
MAPE: 19.04%
MAE: 4004129.91
==================================================
ARIMA(0, 0, 3)
MAPE: 19.4%
MAE: 4118298.46
==================================================
ARIMA(0, 0, 4)
MAPE: 19.56%
MAE: 4120148.67
==================================================
ARIMA(0, 1, 0)
MAPE: 11.06%
MAE: 2215606.13
==================================================
ARIMA(0, 1, 1)
MAPE: 11.1%
MAE: 2426644.36
==================================================
ARIMA(0, 1, 2)
MAPE: 10.23%
MAE: 2224226.45
==================================================
ARIMA(0, 1, 3)
MAPE: 9.5%
MAE: 2120434.29
==================================================
ARIMA(0, 1, 4)
MAPE: 9.44%
MAE: 2110927.8
==================================================
ARIMA(1, 0, 0)
MAPE: 15.36%
MAE: 3301037.74
==================================================
ARIMA(1, 0, 1)
MAPE: 14.69%
MAE: 3142751.21
==================================================
ARIMA(1, 0, 2)
MAPE: 14.05%
MAE: 3026833.07
==================================================
ARIMA(1, 0, 3)
MAPE: 12.28%
MAE: 2683952.27
==================================================
ARIMA(1, 0, 4)
MAPE: 12.22%
MAE: 2673142.07
==================================================
ARIMA(1, 1, 0)
MAPE: 9.17%
MAE: 2017376.97
==================================================
ARIMA(1, 1, 1)
MAPE: 9.53%
MAE: 2092416.1
==================================================
ARIMA(1, 1, 2)
MAPE: 9.71%
MAE: 2131379.5
==================================================
ARIMA(1, 1, 3)
MAPE: 9.44%
MAE: 2111991.97
==================================================
ARIMA(1, 1, 4)
MAPE: 9.43%
MAE: 2109609.6
==================================================
ARIMA(2, 0, 0)
MAPE: 14.52%
MAE: 3104548.96
==================================================
ARIMA(2, 0, 1)
MAPE: 14.49%
MAE: 3097325.19
==================================================
ARIMA(2, 0, 2)
MAPE: 14.29%
MAE: 3064630.96
==================================================
ARIMA(2, 0, 3)
MAPE: 12.21%
MAE: 2670698.15
==================================================
ARIMA(2, 0, 4)
MAPE: 11.96%
MAE: 2625772.04
==================================================
ARIMA(2, 1, 0)
MAPE: 9.5%
MAE: 2084685.19
==================================================
ARIMA(2, 1, 1)
MAPE: 9.99%
MAE: 2195209.06
==================================================
ARIMA(2, 1, 2)
MAPE: 9.6%
MAE: 2102132.9
==================================================
ARIMA(2, 1, 3)
MAPE: 9.51%
MAE: 2115214.77
==================================================
ARIMA(2, 1, 4)
MAPE: 9.22%
MAE: 2082443.37
==================================================
ARIMA(3, 0, 0)
MAPE: 14.45%
MAE: 3089326.37
==================================================
ARIMA(3, 0, 1)
MAPE: 14.44%
MAE: 3089617.41
==================================================
ARIMA(3, 0, 2)
MAPE: 14.52%
MAE: 3137315.66
==================================================
ARIMA(3, 0, 3)
MAPE: 14.12%
MAE: 3049396.3
==================================================
ARIMA(3, 0, 4)
MAPE: 12.27%
MAE: 2685271.32
==================================================
ARIMA(3, 1, 0)
MAPE: 9.52%
MAE: 2089560.16
==================================================
ARIMA(3, 1, 1)
MAPE: 9.12%
MAE: 2025334.16
==================================================
ARIMA(3, 1, 2)
MAPE: 10.15%
MAE: 2231904.92
==================================================
ARIMA(3, 1, 3)
MAPE: 9.28%
MAE: 2078885.94
==================================================
ARIMA(3, 1, 4)
MAPE: 9.29%
MAE: 2081551.09
==================================================
ARIMA(4, 0, 0)
MAPE: 14.32%
MAE: 3073528.26
==================================================
ARIMA(4, 0, 1)
MAPE: 14.4%
MAE: 3100034.37
==================================================
ARIMA(4, 0, 2)
MAPE: 14.33%
MAE: 3111347.72
==================================================
ARIMA(4, 0, 3)
MAPE: 11.7%
MAE: 2586147.0
==================================================
ARIMA(4, 0, 4)
MAPE: 11.82%
MAE: 2601350.49
==================================================
ARIMA(4, 1, 0)
MAPE: 10.66%
MAE: 2371799.52
==================================================
ARIMA(4, 1, 1)
MAPE: 10.66%
MAE: 2365179.88
==================================================
ARIMA(4, 1, 2)
MAPE: 10.1%
MAE: 2220609.78
==================================================
ARIMA(4, 1, 3)
MAPE: 11.57%
MAE: 2283548.38
==================================================
ARIMA(4, 1, 4)
MAPE: 12.21%
MAE: 2403476.32
==================================================

print(f"Best parameters model for JAWA TIMUR:", best_param_jawa_timur)
print(f"with the lowest MAPE: {lowest_mape_jawa_timur.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_jawa_timur.round(2)}")

Best parameters model for JAWA TIMUR: (3, 1, 1)
with the lowest MAPE: 9.12%
with the lowest MAE: 2025334.16

plot_predictions_vs_actual(prediction_series=prediction_jawa_timur, actual_series=test_jawa_timur)

train_banten = training_data["BANTEN"]
test_banten = test_data["BANTEN"]

best_param_banten, lowest_mape_banten, lowest_mae_banten, prediction_banten = make_arima_prediction(train_banten, test_banten)

all_provinces_predictions['BANTEN'] = prediction_banten

ARIMA(0, 0, 0)
MAPE: 20.73%
MAE: 863211.73
==================================================
ARIMA(0, 0, 1)
MAPE: 17.83%
MAE: 748515.49
==================================================
ARIMA(0, 0, 2)
MAPE: 16.42%
MAE: 693436.25
==================================================
ARIMA(0, 0, 3)
MAPE: 15.57%
MAE: 662007.21
==================================================
ARIMA(0, 0, 4)
MAPE: 15.62%
MAE: 663727.36
==================================================
ARIMA(0, 1, 0)
MAPE: 19.04%
MAE: 746137.45
==================================================
ARIMA(0, 1, 1)
MAPE: 16.16%
MAE: 637383.34
==================================================
ARIMA(0, 1, 2)
MAPE: 15.92%
MAE: 628399.56
==================================================
ARIMA(0, 1, 3)
MAPE: 13.1%
MAE: 525497.51
==================================================
ARIMA(0, 1, 4)
MAPE: 8.66%
MAE: 367056.84
==================================================
ARIMA(1, 0, 0)
MAPE: 12.24%
MAE: 522266.73
==================================================
ARIMA(1, 0, 1)
MAPE: 12.52%
MAE: 533641.2
==================================================
ARIMA(1, 0, 2)
MAPE: 15.03%
MAE: 637314.13
==================================================
ARIMA(1, 0, 3)
MAPE: 13.94%
MAE: 591587.51
==================================================
ARIMA(1, 0, 4)
MAPE: 14.02%
MAE: 596065.22
==================================================
ARIMA(1, 1, 0)
MAPE: 16.62%
MAE: 654411.72
==================================================
ARIMA(1, 1, 1)
MAPE: 16.91%
MAE: 665073.08
==================================================
ARIMA(1, 1, 2)
MAPE: 16.53%
MAE: 651155.59
==================================================
ARIMA(1, 1, 3)
MAPE: 9.09%
MAE: 383054.21
==================================================
ARIMA(1, 1, 4)
MAPE: 11.4%
MAE: 463555.27
==================================================
ARIMA(2, 0, 0)
MAPE: 12.65%
MAE: 539298.11
==================================================
ARIMA(2, 0, 1)
MAPE: 11.93%
MAE: 509427.75
==================================================
ARIMA(2, 0, 2)
MAPE: 14.81%
MAE: 627664.55
==================================================
ARIMA(2, 0, 3)
MAPE: 15.16%
MAE: 641786.72
==================================================
ARIMA(2, 0, 4)
MAPE: 13.82%
MAE: 584946.67
==================================================
ARIMA(2, 1, 0)
MAPE: 16.69%
MAE: 657115.29
==================================================
ARIMA(2, 1, 1)
MAPE: 9.22%
MAE: 381263.43
==================================================
ARIMA(2, 1, 2)
MAPE: 8.73%
MAE: 366124.33
==================================================
ARIMA(2, 1, 3)
MAPE: 9.06%
MAE: 380434.83
==================================================
ARIMA(2, 1, 4)
MAPE: 11.92%
MAE: 482543.82
==================================================
ARIMA(3, 0, 0)
MAPE: 14.53%
MAE: 616459.81
==================================================
ARIMA(3, 0, 1)
MAPE: 14.47%
MAE: 613564.65
==================================================
ARIMA(3, 0, 2)
MAPE: 14.18%
MAE: 603808.87
==================================================
ARIMA(3, 0, 3)
MAPE: 12.7%
MAE: 538765.84
==================================================
ARIMA(3, 0, 4)
MAPE: 14.01%
MAE: 590849.48
==================================================
ARIMA(3, 1, 0)
MAPE: 15.6%
MAE: 617075.02
==================================================
ARIMA(3, 1, 1)
MAPE: 13.84%
MAE: 552587.53
==================================================
ARIMA(3, 1, 2)
MAPE: 15.66%
MAE: 616926.26
==================================================
ARIMA(3, 1, 3)
MAPE: 7.94%
MAE: 330121.62
==================================================
ARIMA(3, 1, 4)
MAPE: 8.66%
MAE: 369511.74
==================================================
ARIMA(4, 0, 0)
MAPE: 13.81%
MAE: 586150.05
==================================================
ARIMA(4, 0, 1)
MAPE: 13.56%
MAE: 576012.82
==================================================
ARIMA(4, 0, 2)
MAPE: 12.59%
MAE: 537535.72
==================================================
ARIMA(4, 0, 3)
MAPE: 14.35%
MAE: 609798.96
==================================================
ARIMA(4, 0, 4)
MAPE: 13.95%
MAE: 588733.74
==================================================
ARIMA(4, 1, 0)
MAPE: 13.11%
MAE: 526859.79
==================================================
ARIMA(4, 1, 1)
MAPE: 13.14%
MAE: 526256.4
==================================================
ARIMA(4, 1, 2)
MAPE: 13.08%
MAE: 518741.59
==================================================
ARIMA(4, 1, 3)
MAPE: 7.66%
MAE: 321860.43
==================================================
ARIMA(4, 1, 4)
MAPE: 7.39%
MAE: 315266.31
==================================================

print(f"Best parameters model for BANTEN:", best_param_banten)
print(f"with the lowest MAPE: {lowest_mape_banten.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_banten.round(2)}")

Best parameters model for BANTEN: (4, 1, 4)
with the lowest MAPE: 7.39%
with the lowest MAE: 315266.31

plot_predictions_vs_actual(prediction_series=prediction_banten, actual_series=test_banten)

train_bali = training_data["BALI"]
test_bali = test_data["BALI"]

best_param_bali, lowest_mape_bali, lowest_mae_bali, prediction_bali = make_arima_prediction(train_bali, test_bali)

all_provinces_predictions['BALI'] = prediction_bali

ARIMA(0, 0, 0)
MAPE: 35.51%
MAE: 688914.85
==================================================
ARIMA(0, 0, 1)
MAPE: 32.1%
MAE: 621502.4
==================================================
ARIMA(0, 0, 2)
MAPE: 28.89%
MAE: 557916.13
==================================================
ARIMA(0, 0, 3)
MAPE: 26.92%
MAE: 519950.16
==================================================
ARIMA(0, 0, 4)
MAPE: 25.32%
MAE: 488359.76
==================================================
ARIMA(0, 1, 0)
MAPE: 4.17%
MAE: 78647.39
==================================================
ARIMA(0, 1, 1)
MAPE: 3.34%
MAE: 62804.22
==================================================
ARIMA(0, 1, 2)
MAPE: 3.19%
MAE: 60112.97
==================================================
ARIMA(0, 1, 3)
MAPE: 2.82%
MAE: 53081.7
==================================================
ARIMA(0, 1, 4)
MAPE: 2.97%
MAE: 57196.2
==================================================
ARIMA(1, 0, 0)
MAPE: 11.74%
MAE: 227629.49
==================================================
ARIMA(1, 0, 1)
MAPE: 10.8%
MAE: 209669.59
==================================================
ARIMA(1, 0, 2)
MAPE: 9.86%
MAE: 191705.41
==================================================
ARIMA(1, 0, 3)
MAPE: 9.71%
MAE: 188728.87
==================================================
ARIMA(1, 0, 4)
MAPE: 9.54%
MAE: 185539.13
==================================================
ARIMA(1, 1, 0)
MAPE: 3.42%
MAE: 64342.47
==================================================
ARIMA(1, 1, 1)
MAPE: 3.12%
MAE: 61462.91
==================================================
ARIMA(1, 1, 2)
MAPE: 2.97%
MAE: 58666.92
==================================================
ARIMA(1, 1, 3)
MAPE: 2.68%
MAE: 50532.92
==================================================
ARIMA(1, 1, 4)
MAPE: 3.19%
MAE: 61254.48
==================================================
ARIMA(2, 0, 0)
MAPE: 10.91%
MAE: 211794.55
==================================================
ARIMA(2, 0, 1)
MAPE: 9.92%
MAE: 192737.19
==================================================
ARIMA(2, 0, 2)
MAPE: 9.6%
MAE: 186727.34
==================================================
ARIMA(2, 0, 3)
MAPE: 9.71%
MAE: 188734.94
==================================================
ARIMA(2, 0, 4)
MAPE: 9.55%
MAE: 185793.83
==================================================
ARIMA(2, 1, 0)
MAPE: 3.35%
MAE: 63021.35
==================================================
ARIMA(2, 1, 1)
MAPE: 2.95%
MAE: 58197.48
==================================================
ARIMA(2, 1, 2)
MAPE: 3.08%
MAE: 60787.85
==================================================
ARIMA(2, 1, 3)
MAPE: 3.67%
MAE: 69221.12
==================================================
ARIMA(2, 1, 4)
MAPE: 3.41%
MAE: 64254.66
==================================================
ARIMA(3, 0, 0)
MAPE: 10.3%
MAE: 200050.4
==================================================
ARIMA(3, 0, 1)
MAPE: 9.72%
MAE: 189011.66
==================================================
ARIMA(3, 0, 2)
MAPE: 10.5%
MAE: 202903.87
==================================================
ARIMA(3, 0, 3)
MAPE: 11.83%
MAE: 228373.71
==================================================
ARIMA(3, 0, 4)
MAPE: 9.6%
MAE: 186443.03
==================================================
ARIMA(3, 1, 0)
MAPE: 3.04%
MAE: 57055.41
==================================================
ARIMA(3, 1, 1)
MAPE: 2.68%
MAE: 50430.7
==================================================
ARIMA(3, 1, 2)
MAPE: 3.35%
MAE: 64578.39
==================================================
ARIMA(3, 1, 3)
MAPE: 2.37%
MAE: 46366.68
==================================================
ARIMA(3, 1, 4)
MAPE: 3.48%
MAE: 65561.45
==================================================
ARIMA(4, 0, 0)
MAPE: 9.89%
MAE: 192286.65
==================================================
ARIMA(4, 0, 1)
MAPE: 9.51%
MAE: 184889.76
==================================================
ARIMA(4, 0, 2)
MAPE: 11.72%
MAE: 226186.04
==================================================
ARIMA(4, 0, 3)
MAPE: 10.63%
MAE: 205400.95
==================================================
ARIMA(4, 0, 4)
MAPE: 9.51%
MAE: 184643.85
==================================================
ARIMA(4, 1, 0)
MAPE: 2.67%
MAE: 50669.14
==================================================
ARIMA(4, 1, 1)
MAPE: 2.73%
MAE: 51775.19
==================================================
ARIMA(4, 1, 2)
MAPE: 3.85%
MAE: 72735.67
==================================================
ARIMA(4, 1, 3)
MAPE: 3.67%
MAE: 69458.69
==================================================
ARIMA(4, 1, 4)
MAPE: 3.25%
MAE: 64181.66
==================================================

print(f"Best parameters model for BALI:", best_param_bali)
print(f"with the lowest MAPE: {lowest_mape_bali.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_bali.round(2)}")

Best parameters model for BALI: (3, 1, 3)
with the lowest MAPE: 2.37%
with the lowest MAE: 46366.68

plot_predictions_vs_actual(prediction_series=prediction_bali, actual_series=test_bali)

train_nusa_tenggara_barat = training_data["NUSA TENGGARA BARAT"]
test_nusa_tenggara_barat = test_data["NUSA TENGGARA BARAT"]

best_param_nusa_tenggara_barat, lowest_mape_nusa_tenggara_barat, lowest_mae_nusa_tenggara_barat, prediction_nusa_tenggara_barat = make_arima_prediction(train_nusa_tenggara_barat, test_nusa_tenggara_barat)

all_provinces_predictions['NUSA TENGGARA BARAT'] = prediction_nusa_tenggara_barat

ARIMA(0, 0, 0)
MAPE: 53.22%
MAE: 583458.07
==================================================
ARIMA(0, 0, 1)
MAPE: 51.64%
MAE: 565744.32
==================================================
ARIMA(0, 0, 2)
MAPE: 51.57%
MAE: 563503.94
==================================================
ARIMA(0, 0, 3)
MAPE: 58.81%
MAE: 638680.75
==================================================
ARIMA(0, 0, 4)
MAPE: 64.77%
MAE: 701415.45
==================================================
ARIMA(0, 1, 0)
MAPE: 32.8%
MAE: 362589.61
==================================================
ARIMA(0, 1, 1)
MAPE: 39.53%
MAE: 435555.72
==================================================
ARIMA(0, 1, 2)
MAPE: 35.01%
MAE: 386433.49
==================================================
ARIMA(0, 1, 3)
MAPE: 31.98%
MAE: 353047.38
==================================================
ARIMA(0, 1, 4)
MAPE: 31.71%
MAE: 349263.14
==================================================
ARIMA(1, 0, 0)
MAPE: 34.89%
MAE: 384372.07
==================================================
ARIMA(1, 0, 1)
MAPE: 43.62%
MAE: 478141.49
==================================================
ARIMA(1, 0, 2)
MAPE: 51.27%
MAE: 559969.86
==================================================
ARIMA(1, 0, 3)
MAPE: 44.44%
MAE: 486215.5
==================================================
ARIMA(1, 0, 4)
MAPE: 44.36%
MAE: 485174.88
==================================================
ARIMA(1, 1, 0)
MAPE: 39.16%
MAE: 431580.05
==================================================
ARIMA(1, 1, 1)
MAPE: 38.25%
MAE: 421660.87
==================================================
ARIMA(1, 1, 2)
MAPE: 44.28%
MAE: 487402.39
==================================================
ARIMA(1, 1, 3)
MAPE: 31.7%
MAE: 349735.16
==================================================
ARIMA(1, 1, 4)
MAPE: 31.81%
MAE: 350426.2
==================================================
ARIMA(2, 0, 0)
MAPE: 49.43%
MAE: 540966.66
==================================================
ARIMA(2, 0, 1)
MAPE: 47.13%
MAE: 515913.18
==================================================
ARIMA(2, 0, 2)
MAPE: 48.4%
MAE: 529140.3
==================================================
ARIMA(2, 0, 3)
MAPE: 44.42%
MAE: 485902.26
==================================================
ARIMA(2, 0, 4)
MAPE: 73.27%
MAE: 804207.3
==================================================
ARIMA(2, 1, 0)
MAPE: 33.93%
MAE: 374682.18
==================================================
ARIMA(2, 1, 1)
MAPE: 46.22%
MAE: 508441.11
==================================================
ARIMA(2, 1, 2)
MAPE: 45.35%
MAE: 496262.53
==================================================
ARIMA(2, 1, 3)
MAPE: 45.19%
MAE: 494492.69
==================================================
ARIMA(2, 1, 4)
MAPE: 46.65%
MAE: 510412.94
==================================================
ARIMA(3, 0, 0)
MAPE: 43.94%
MAE: 481154.23
==================================================
ARIMA(3, 0, 1)
MAPE: 43.17%
MAE: 472691.91
==================================================
ARIMA(3, 0, 2)
MAPE: 47.81%
MAE: 520141.19
==================================================
ARIMA(3, 0, 3)
MAPE: 49.35%
MAE: 536847.98
==================================================
ARIMA(3, 0, 4)
MAPE: 49.76%
MAE: 540728.42
==================================================
ARIMA(3, 1, 0)
MAPE: 31.32%
MAE: 345784.81
==================================================
ARIMA(3, 1, 1)
MAPE: 31.91%
MAE: 352256.92
==================================================
ARIMA(3, 1, 2)
MAPE: 44.74%
MAE: 489633.54
==================================================
ARIMA(3, 1, 3)
MAPE: 49.59%
MAE: 541409.56
==================================================
ARIMA(3, 1, 4)
MAPE: 47.85%
MAE: 523028.25
==================================================
ARIMA(4, 0, 0)
MAPE: 40.88%
MAE: 447362.25
==================================================
ARIMA(4, 0, 1)
MAPE: 42.17%
MAE: 461968.9
==================================================
ARIMA(4, 0, 2)
MAPE: 50.37%
MAE: 546956.79
==================================================
ARIMA(4, 0, 3)
MAPE: 48.9%
MAE: 531948.42
==================================================
ARIMA(4, 0, 4)
MAPE: 93.79%
MAE: 1022371.55
==================================================
ARIMA(4, 1, 0)
MAPE: 34.85%
MAE: 384622.33
==================================================
ARIMA(4, 1, 1)
MAPE: 37.28%
MAE: 411222.87
==================================================
ARIMA(4, 1, 2)
MAPE: 46.01%
MAE: 503514.77
==================================================
ARIMA(4, 1, 3)
MAPE: 44.43%
MAE: 486303.81
==================================================
ARIMA(4, 1, 4)
MAPE: 53.15%
MAE: 580265.73
==================================================

print(f"Best parameters model for NUSA TENGGARA BARAT:", best_param_nusa_tenggara_barat)
print(f"with the lowest MAPE: {lowest_mape_nusa_tenggara_barat.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_nusa_tenggara_barat.round(2)}")

Best parameters model for NUSA TENGGARA BARAT: (3, 1, 0)
with the lowest MAPE: 31.32%
with the lowest MAE: 345784.81

plot_predictions_vs_actual(prediction_series=prediction_nusa_tenggara_barat, actual_series=test_nusa_tenggara_barat)

train_nusa_tenggara_timur = training_data["NUSA TENGGARA TIMUR"]
test_nusa_tenggara_timur = test_data["NUSA TENGGARA TIMUR"]

best_param_nusa_tenggara_timur, lowest_mape_nusa_tenggara_timur, lowest_mae_nusa_tenggara_timur, prediction_nusa_tenggara_timur = make_arima_prediction(train_nusa_tenggara_timur, test_nusa_tenggara_timur)

all_provinces_predictions['NUSA TENGGARA TIMUR'] = prediction_nusa_tenggara_timur

ARIMA(0, 0, 0)
MAPE: 43.85%
MAE: 281686.59
==================================================
ARIMA(0, 0, 1)
MAPE: 37.64%
MAE: 238099.51
==================================================
ARIMA(0, 0, 2)
MAPE: 32.02%
MAE: 202419.47
==================================================
ARIMA(0, 0, 3)
MAPE: 28.47%
MAE: 179665.66
==================================================
ARIMA(0, 0, 4)
MAPE: 26.43%
MAE: 167403.19
==================================================
ARIMA(0, 1, 0)
MAPE: 20.77%
MAE: 130481.07
==================================================
ARIMA(0, 1, 1)
MAPE: 4.56%
MAE: 30388.01
==================================================
ARIMA(0, 1, 2)
MAPE: 9.0%
MAE: 58534.01
==================================================
ARIMA(0, 1, 3)
MAPE: 12.61%
MAE: 81321.81
==================================================
ARIMA(0, 1, 4)
MAPE: 16.54%
MAE: 105664.43
==================================================
ARIMA(1, 0, 0)
MAPE: 17.83%
MAE: 114146.62
==================================================
ARIMA(1, 0, 1)
MAPE: 15.51%
MAE: 98717.18
==================================================
ARIMA(1, 0, 2)
MAPE: 15.99%
MAE: 101854.5
==================================================
ARIMA(1, 0, 3)
MAPE: 15.58%
MAE: 99761.61
==================================================
ARIMA(1, 0, 4)
MAPE: 16.07%
MAE: 102797.59
==================================================
ARIMA(1, 1, 0)
MAPE: 9.4%
MAE: 59005.34
==================================================
ARIMA(1, 1, 1)
MAPE: 23.88%
MAE: 151028.84
==================================================
ARIMA(1, 1, 2)
MAPE: 21.51%
MAE: 136804.01
==================================================
ARIMA(1, 1, 3)
MAPE: 20.72%
MAE: 131709.55
==================================================
ARIMA(1, 1, 4)
MAPE: 16.11%
MAE: 103142.59
==================================================
ARIMA(2, 0, 0)
MAPE: 15.41%
MAE: 98292.85
==================================================
ARIMA(2, 0, 1)
MAPE: 16.04%
MAE: 102158.9
==================================================
ARIMA(2, 0, 2)
MAPE: 15.99%
MAE: 101949.1
==================================================
ARIMA(2, 0, 3)
MAPE: 15.6%
MAE: 99849.13
==================================================
ARIMA(2, 0, 4)
MAPE: 13.16%
MAE: 85032.43
==================================================
ARIMA(2, 1, 0)
MAPE: 4.68%
MAE: 29954.97
==================================================
ARIMA(2, 1, 1)
MAPE: 21.44%
MAE: 136233.44
==================================================
ARIMA(2, 1, 2)
MAPE: 21.31%
MAE: 135578.62
==================================================
ARIMA(2, 1, 3)
MAPE: 11.6%
MAE: 75991.35
==================================================
ARIMA(2, 1, 4)
MAPE: 18.66%
MAE: 121153.74
==================================================
ARIMA(3, 0, 0)
MAPE: 15.54%
MAE: 99077.26
==================================================
ARIMA(3, 0, 1)
MAPE: 15.37%
MAE: 98012.14
==================================================
ARIMA(3, 0, 2)
MAPE: 15.6%
MAE: 99507.97
==================================================
ARIMA(3, 0, 3)
MAPE: 17.59%
MAE: 112426.09
==================================================
ARIMA(3, 0, 4)
MAPE: 12.88%
MAE: 83233.3
==================================================
ARIMA(3, 1, 0)
MAPE: 4.71%
MAE: 31482.69
==================================================
ARIMA(3, 1, 1)
MAPE: 9.8%
MAE: 63705.29
==================================================
ARIMA(3, 1, 2)
MAPE: 16.96%
MAE: 110281.98
==================================================
ARIMA(3, 1, 3)
MAPE: 20.17%
MAE: 131077.34
==================================================
ARIMA(3, 1, 4)
MAPE: 11.18%
MAE: 73174.7
==================================================
ARIMA(4, 0, 0)
MAPE: 16.21%
MAE: 103483.48
==================================================
ARIMA(4, 0, 1)
MAPE: 16.64%
MAE: 106241.81
==================================================
ARIMA(4, 0, 2)
MAPE: 12.8%
MAE: 82389.35
==================================================
ARIMA(4, 0, 3)
MAPE: 15.4%
MAE: 98569.77
==================================================
ARIMA(4, 0, 4)
MAPE: 12.94%
MAE: 83796.85
==================================================
ARIMA(4, 1, 0)
MAPE: 10.82%
MAE: 70571.97
==================================================
ARIMA(4, 1, 1)
MAPE: 11.03%
MAE: 71884.64
==================================================
ARIMA(4, 1, 2)
MAPE: 16.65%
MAE: 108406.1
==================================================
ARIMA(4, 1, 3)
MAPE: 13.1%
MAE: 85202.81
==================================================
ARIMA(4, 1, 4)
MAPE: 20.7%
MAE: 132615.83
==================================================

print(f"Best parameters model for NUSA TENGGARA TIMUR:", best_param_nusa_tenggara_timur)
print(f"with the lowest MAPE: {lowest_mape_nusa_tenggara_timur.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_nusa_tenggara_timur.round(2)}")

Best parameters model for NUSA TENGGARA TIMUR: (0, 1, 1)
with the lowest MAPE: 4.56%
with the lowest MAE: 30388.01

plot_predictions_vs_actual(prediction_series=prediction_nusa_tenggara_timur, actual_series=test_nusa_tenggara_timur)

train_kalimantan_barat = training_data["KALIMANTAN BARAT"]
test_kalimantan_barat = test_data["KALIMANTAN BARAT"]

best_param_kalimantan_barat, lowest_mape_kalimantan_barat, lowest_mae_kalimantan_barat, prediction_kalimantan_barat = make_arima_prediction(train_kalimantan_barat, test_kalimantan_barat)

all_provinces_predictions['KALIMANTAN BARAT'] = prediction_kalimantan_barat

ARIMA(0, 0, 0)
MAPE: 48.87%
MAE: 339001.62
==================================================
ARIMA(0, 0, 1)
MAPE: 42.49%
MAE: 294681.29
==================================================
ARIMA(0, 0, 2)
MAPE: 36.7%
MAE: 256454.96
==================================================
ARIMA(0, 0, 3)
MAPE: 28.58%
MAE: 203536.83
==================================================
ARIMA(0, 0, 4)
MAPE: 24.85%
MAE: 175590.88
==================================================
ARIMA(0, 1, 0)
MAPE: 15.27%
MAE: 102302.84
==================================================
ARIMA(0, 1, 1)
MAPE: 8.49%
MAE: 64598.47
==================================================
ARIMA(0, 1, 2)
MAPE: 9.22%
MAE: 69809.67
==================================================
ARIMA(0, 1, 3)
MAPE: 10.63%
MAE: 79948.58
==================================================
ARIMA(0, 1, 4)
MAPE: 10.24%
MAE: 76945.58
==================================================
ARIMA(1, 0, 0)
MAPE: 8.07%
MAE: 61169.61
==================================================
ARIMA(1, 0, 1)
MAPE: 7.42%
MAE: 57014.0
==================================================
ARIMA(1, 0, 2)
MAPE: 5.6%
MAE: 44354.9
==================================================
ARIMA(1, 0, 3)
MAPE: 6.01%
MAE: 47401.59
==================================================
ARIMA(1, 0, 4)
MAPE: 6.96%
MAE: 52762.89
==================================================
ARIMA(1, 1, 0)
MAPE: 8.37%
MAE: 61504.16
==================================================
ARIMA(1, 1, 1)
MAPE: 9.55%
MAE: 72177.48
==================================================
ARIMA(1, 1, 2)
MAPE: 9.71%
MAE: 73365.42
==================================================
ARIMA(1, 1, 3)
MAPE: 10.53%
MAE: 79201.48
==================================================
ARIMA(1, 1, 4)
MAPE: 8.85%
MAE: 67515.87
==================================================
ARIMA(2, 0, 0)
MAPE: 6.26%
MAE: 49191.98
==================================================
ARIMA(2, 0, 1)
MAPE: 5.76%
MAE: 45653.73
==================================================
ARIMA(2, 0, 2)
MAPE: 5.69%
MAE: 44918.36
==================================================
ARIMA(2, 0, 3)
MAPE: 5.27%
MAE: 41968.89
==================================================
ARIMA(2, 0, 4)
MAPE: 5.2%
MAE: 40472.76
==================================================
ARIMA(2, 1, 0)
MAPE: 7.97%
MAE: 59808.26
==================================================
ARIMA(2, 1, 1)
MAPE: 9.59%
MAE: 72531.76
==================================================
ARIMA(2, 1, 2)
MAPE: 9.43%
MAE: 71543.78
==================================================
ARIMA(2, 1, 3)
MAPE: 10.43%
MAE: 79288.69
==================================================
ARIMA(2, 1, 4)
MAPE: 11.43%
MAE: 85055.36
==================================================
ARIMA(3, 0, 0)
MAPE: 5.52%
MAE: 44070.55
==================================================
ARIMA(3, 0, 1)
MAPE: 5.82%
MAE: 45865.98
==================================================
ARIMA(3, 0, 2)
MAPE: 5.62%
MAE: 44476.87
==================================================
ARIMA(3, 0, 3)
MAPE: 6.59%
MAE: 51560.24
==================================================
ARIMA(3, 0, 4)
MAPE: 11.25%
MAE: 82319.75
==================================================
ARIMA(3, 1, 0)
MAPE: 9.69%
MAE: 73097.22
==================================================
ARIMA(3, 1, 1)
MAPE: 11.11%
MAE: 82902.1
==================================================
ARIMA(3, 1, 2)
MAPE: 12.2%
MAE: 90097.9
==================================================
ARIMA(3, 1, 3)
MAPE: 12.36%
MAE: 90831.04
==================================================
ARIMA(3, 1, 4)
MAPE: 12.0%
MAE: 88374.68
==================================================
ARIMA(4, 0, 0)
MAPE: 6.31%
MAE: 49380.05
==================================================
ARIMA(4, 0, 1)
MAPE: 6.12%
MAE: 47827.17
==================================================
ARIMA(4, 0, 2)
MAPE: 5.68%
MAE: 44855.41
==================================================
ARIMA(4, 0, 3)
MAPE: 5.27%
MAE: 41916.09
==================================================
ARIMA(4, 0, 4)
MAPE: 8.35%
MAE: 63223.36
==================================================
ARIMA(4, 1, 0)
MAPE: 13.07%
MAE: 96745.88
==================================================
ARIMA(4, 1, 1)
MAPE: 12.8%
MAE: 95042.0
==================================================
ARIMA(4, 1, 2)
MAPE: 11.49%
MAE: 84101.01
==================================================
ARIMA(4, 1, 3)
MAPE: 12.6%
MAE: 93184.41
==================================================
ARIMA(4, 1, 4)
MAPE: 17.21%
MAE: 124992.75
==================================================

print(f"Best parameters model for KALIMANTAN BARAT:", best_param_kalimantan_barat)
print(f"with the lowest MAPE: {lowest_mape_kalimantan_barat.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_kalimantan_barat.round(2)}")

Best parameters model for KALIMANTAN BARAT: (2, 0, 4)
with the lowest MAPE: 5.2%
with the lowest MAE: 40472.76

plot_predictions_vs_actual(prediction_series=prediction_kalimantan_barat, actual_series=test_kalimantan_barat)

train_kalimantan_tengah = training_data["KALIMANTAN TENGAH"]
test_kalimantan_tengah = test_data["KALIMANTAN TENGAH"]

best_param_kalimantan_tengah, lowest_mape_kalimantan_tengah, lowest_mae_kalimantan_tengah, prediction_kalimantan_tengah = make_arima_prediction(train_kalimantan_tengah, test_kalimantan_tengah)

all_provinces_predictions['KALIMANTAN TENGAH'] = prediction_kalimantan_tengah

ARIMA(0, 0, 0)
MAPE: 49.81%
MAE: 277107.66
==================================================
ARIMA(0, 0, 1)
MAPE: 42.94%
MAE: 236349.39
==================================================
ARIMA(0, 0, 2)
MAPE: 35.39%
MAE: 195300.07
==================================================
ARIMA(0, 0, 3)
MAPE: 30.77%
MAE: 173261.59
==================================================
ARIMA(0, 0, 4)
MAPE: 34.56%
MAE: 186854.54
==================================================
ARIMA(0, 1, 0)
MAPE: 58.43%
MAE: 315518.12
==================================================
ARIMA(0, 1, 1)
MAPE: 19.49%
MAE: 102365.62
==================================================
ARIMA(0, 1, 2)
MAPE: 21.84%
MAE: 114923.3
==================================================
ARIMA(0, 1, 3)
MAPE: 20.41%
MAE: 107077.26
==================================================
ARIMA(0, 1, 4)
MAPE: 20.52%
MAE: 107696.41
==================================================
ARIMA(1, 0, 0)
MAPE: 20.31%
MAE: 110899.48
==================================================
ARIMA(1, 0, 1)
MAPE: 9.98%
MAE: 55401.28
==================================================
ARIMA(1, 0, 2)
MAPE: 10.21%
MAE: 55903.46
==================================================
ARIMA(1, 0, 3)
MAPE: 10.31%
MAE: 56588.66
==================================================
ARIMA(1, 0, 4)
MAPE: 10.26%
MAE: 55951.32
==================================================
ARIMA(1, 1, 0)
MAPE: 28.58%
MAE: 151407.8
==================================================
ARIMA(1, 1, 1)
MAPE: 22.57%
MAE: 118778.12
==================================================
ARIMA(1, 1, 2)
MAPE: 15.39%
MAE: 81481.56
==================================================
ARIMA(1, 1, 3)
MAPE: 20.43%
MAE: 107173.65
==================================================
ARIMA(1, 1, 4)
MAPE: 21.78%
MAE: 114391.3
==================================================
ARIMA(2, 0, 0)
MAPE: 11.63%
MAE: 62965.74
==================================================
ARIMA(2, 0, 1)
MAPE: 10.56%
MAE: 57621.84
==================================================
ARIMA(2, 0, 2)
MAPE: 9.89%
MAE: 54191.79
==================================================
ARIMA(2, 0, 3)
MAPE: 9.65%
MAE: 52804.32
==================================================
ARIMA(2, 0, 4)
MAPE: 11.2%
MAE: 61087.64
==================================================
ARIMA(2, 1, 0)
MAPE: 24.73%
MAE: 130481.7
==================================================
ARIMA(2, 1, 1)
MAPE: 21.11%
MAE: 110917.25
==================================================
ARIMA(2, 1, 2)
MAPE: 20.89%
MAE: 109782.06
==================================================
ARIMA(2, 1, 3)
MAPE: 21.35%
MAE: 111890.26
==================================================
ARIMA(2, 1, 4)
MAPE: 21.26%
MAE: 110586.33
==================================================
ARIMA(3, 0, 0)
MAPE: 10.84%
MAE: 59005.12
==================================================
ARIMA(3, 0, 1)
MAPE: 10.46%
MAE: 57140.35
==================================================
ARIMA(3, 0, 2)
MAPE: 9.77%
MAE: 53439.75
==================================================
ARIMA(3, 0, 3)
MAPE: 13.42%
MAE: 73778.42
==================================================
ARIMA(3, 0, 4)
MAPE: 7.5%
MAE: 44144.33
==================================================
ARIMA(3, 1, 0)
MAPE: 21.85%
MAE: 115073.52
==================================================
ARIMA(3, 1, 1)
MAPE: 20.43%
MAE: 107270.53
==================================================
ARIMA(3, 1, 2)
MAPE: 19.48%
MAE: 101388.78
==================================================
ARIMA(3, 1, 3)
MAPE: 18.56%
MAE: 97578.69
==================================================
ARIMA(3, 1, 4)
MAPE: 21.24%
MAE: 111309.34
==================================================
ARIMA(4, 0, 0)
MAPE: 10.4%
MAE: 56781.05
==================================================
ARIMA(4, 0, 1)
MAPE: 10.36%
MAE: 56562.29
==================================================
ARIMA(4, 0, 2)
MAPE: 11.65%
MAE: 63184.74
==================================================
ARIMA(4, 0, 3)
MAPE: 14.7%
MAE: 81710.56
==================================================
ARIMA(4, 0, 4)
MAPE: 6.44%
MAE: 37540.38
==================================================
ARIMA(4, 1, 0)
MAPE: 19.74%
MAE: 103751.13
==================================================
ARIMA(4, 1, 1)
MAPE: 20.75%
MAE: 108723.1
==================================================
ARIMA(4, 1, 2)
MAPE: 19.8%
MAE: 103008.55
==================================================
ARIMA(4, 1, 3)
MAPE: 19.63%
MAE: 102367.03
==================================================
ARIMA(4, 1, 4)
MAPE: 22.66%
MAE: 118477.26
==================================================

print(f"Best parameters model for KALIMANTAN TENGAH:", best_param_kalimantan_tengah)
print(f"with the lowest MAPE: {lowest_mape_kalimantan_tengah.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_kalimantan_tengah.round(2)}")

Best parameters model for KALIMANTAN TENGAH: (4, 0, 4)
with the lowest MAPE: 6.44%
with the lowest MAE: 37540.38

plot_predictions_vs_actual(prediction_series=prediction_kalimantan_tengah, actual_series=test_kalimantan_tengah)

train_kalimantan_selatan = training_data["KALIMANTAN SELATAN"]
test_kalimantan_selatan = test_data["KALIMANTAN SELATAN"]

best_param_kalimantan_selatan, lowest_mape_kalimantan_selatan, lowest_mae_kalimantan_selatan, prediction_kalimantan_selatan = make_arima_prediction(train_kalimantan_selatan, test_kalimantan_selatan)

all_provinces_predictions['KALIMANTAN SELATAN'] = prediction_kalimantan_selatan

ARIMA(0, 0, 0)
MAPE: 42.33%
MAE: 395138.67
==================================================
ARIMA(0, 0, 1)
MAPE: 34.54%
MAE: 311672.9
==================================================
ARIMA(0, 0, 2)
MAPE: 42.02%
MAE: 372109.71
==================================================
ARIMA(0, 0, 3)
MAPE: 35.46%
MAE: 316346.45
==================================================
ARIMA(0, 0, 4)
MAPE: 39.86%
MAE: 344066.67
==================================================
ARIMA(0, 1, 0)
MAPE: 98.55%
MAE: 868808.76
==================================================
ARIMA(0, 1, 1)
MAPE: 34.95%
MAE: 298025.47
==================================================
ARIMA(0, 1, 2)
MAPE: 58.15%
MAE: 501467.83
==================================================
ARIMA(0, 1, 3)
MAPE: 44.39%
MAE: 378748.79
==================================================
ARIMA(0, 1, 4)
MAPE: 44.38%
MAE: 378588.65
==================================================
ARIMA(1, 0, 0)
MAPE: 23.14%
MAE: 215045.16
==================================================
ARIMA(1, 0, 1)
MAPE: 11.45%
MAE: 100168.1
==================================================
ARIMA(1, 0, 2)
MAPE: 18.44%
MAE: 158139.41
==================================================
ARIMA(1, 0, 3)
MAPE: 20.36%
MAE: 179583.76
==================================================
ARIMA(1, 0, 4)
MAPE: 23.71%
MAE: 200480.42
==================================================
ARIMA(1, 1, 0)
MAPE: 50.9%
MAE: 436394.44
==================================================
ARIMA(1, 1, 1)
MAPE: 55.02%
MAE: 473599.0
==================================================
ARIMA(1, 1, 2)
MAPE: 68.96%
MAE: 596469.06
==================================================
ARIMA(1, 1, 3)
MAPE: 44.38%
MAE: 378628.38
==================================================
ARIMA(1, 1, 4)
MAPE: 56.1%
MAE: 486345.33
==================================================
ARIMA(2, 0, 0)
MAPE: 16.58%
MAE: 144037.13
==================================================
ARIMA(2, 0, 1)
MAPE: 16.71%
MAE: 147625.93
==================================================
ARIMA(2, 0, 2)
MAPE: 20.43%
MAE: 175991.48
==================================================
ARIMA(2, 0, 3)
MAPE: 20.49%
MAE: 180267.1
==================================================
ARIMA(2, 0, 4)
MAPE: 30.6%
MAE: 267393.07
==================================================
ARIMA(2, 1, 0)
MAPE: 56.4%
MAE: 485823.65
==================================================
ARIMA(2, 1, 1)
MAPE: 49.75%
MAE: 421810.04
==================================================
ARIMA(2, 1, 2)
MAPE: 106.92%
MAE: 927556.73
==================================================
ARIMA(2, 1, 3)
MAPE: 36.78%
MAE: 310401.14
==================================================
ARIMA(2, 1, 4)
MAPE: 52.68%
MAE: 447868.58
==================================================
ARIMA(3, 0, 0)
MAPE: 17.69%
MAE: 157344.08
==================================================
ARIMA(3, 0, 1)
MAPE: 22.63%
MAE: 201404.73
==================================================
ARIMA(3, 0, 2)
MAPE: 33.2%
MAE: 285224.64
==================================================
ARIMA(3, 0, 3)
MAPE: 14.71%
MAE: 129775.82
==================================================
ARIMA(3, 0, 4)
MAPE: 31.86%
MAE: 280274.19
==================================================
ARIMA(3, 1, 0)
MAPE: 56.66%
MAE: 488079.94
==================================================
ARIMA(3, 1, 1)
MAPE: 35.06%
MAE: 298411.28
==================================================
ARIMA(3, 1, 2)
MAPE: 87.27%
MAE: 755316.3
==================================================
ARIMA(3, 1, 3)
MAPE: 52.43%
MAE: 446816.28
==================================================
ARIMA(3, 1, 4)
MAPE: 51.59%
MAE: 439888.03
==================================================
ARIMA(4, 0, 0)
MAPE: 28.04%
MAE: 242707.58
==================================================
ARIMA(4, 0, 1)
MAPE: 28.7%
MAE: 249462.25
==================================================
ARIMA(4, 0, 2)
MAPE: 33.74%
MAE: 290159.31
==================================================
ARIMA(4, 0, 3)
MAPE: 30.48%
MAE: 262313.58
==================================================
ARIMA(4, 0, 4)
MAPE: 26.05%
MAE: 232914.77
==================================================
ARIMA(4, 1, 0)
MAPE: 34.95%
MAE: 298052.07
==================================================
ARIMA(4, 1, 1)
MAPE: 36.16%
MAE: 307630.59
==================================================
ARIMA(4, 1, 2)
MAPE: 66.34%
MAE: 568328.65
==================================================
ARIMA(4, 1, 3)
MAPE: 54.59%
MAE: 464662.13
==================================================
ARIMA(4, 1, 4)
MAPE: 53.49%
MAE: 455164.67
==================================================

print(f"Best parameters model for KALIMANTAN SELATAN:", best_param_kalimantan_selatan)
print(f"with the lowest MAPE: {lowest_mape_kalimantan_selatan.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_kalimantan_selatan.round(2)}")

Best parameters model for KALIMANTAN SELATAN: (1, 0, 1)
with the lowest MAPE: 11.45%
with the lowest MAE: 100168.1

plot_predictions_vs_actual(prediction_series=prediction_kalimantan_selatan, actual_series=test_kalimantan_selatan)

train_kalimantan_timur = training_data["KALIMANTAN TIMUR"]
test_kalimantan_timur = test_data["KALIMANTAN TIMUR"]

best_param_kalimantan_timur, lowest_mape_kalimantan_timur, lowest_mae_kalimantan_timur, prediction_kalimantan_timur = make_arima_prediction(train_kalimantan_timur, test_kalimantan_timur)

all_provinces_predictions['KALIMANTAN TIMUR'] = prediction_kalimantan_timur

ARIMA(0, 0, 0)
MAPE: 50.48%
MAE: 482114.7
==================================================
ARIMA(0, 0, 1)
MAPE: 41.17%
MAE: 395359.31
==================================================
ARIMA(0, 0, 2)
MAPE: 35.86%
MAE: 347239.21
==================================================
ARIMA(0, 0, 3)
MAPE: 28.79%
MAE: 285502.7
==================================================
ARIMA(0, 0, 4)
MAPE: 27.24%
MAE: 262962.62
==================================================
ARIMA(0, 1, 0)
MAPE: 25.84%
MAE: 233391.45
==================================================
ARIMA(0, 1, 1)
MAPE: 12.62%
MAE: 118145.73
==================================================
ARIMA(0, 1, 2)
MAPE: 14.44%
MAE: 133245.17
==================================================
ARIMA(0, 1, 3)
MAPE: 14.01%
MAE: 130258.02
==================================================
ARIMA(0, 1, 4)
MAPE: 13.96%
MAE: 129805.63
==================================================
ARIMA(1, 0, 0)
MAPE: 13.65%
MAE: 130332.62
==================================================
ARIMA(1, 0, 1)
MAPE: 9.61%
MAE: 95035.86
==================================================
ARIMA(1, 0, 2)
MAPE: 12.26%
MAE: 116971.91
==================================================
ARIMA(1, 0, 3)
MAPE: 11.7%
MAE: 112575.54
==================================================
ARIMA(1, 0, 4)
MAPE: 14.52%
MAE: 137412.85
==================================================
ARIMA(1, 1, 0)
MAPE: 14.34%
MAE: 132507.81
==================================================
ARIMA(1, 1, 1)
MAPE: 14.75%
MAE: 135928.7
==================================================
ARIMA(1, 1, 2)
MAPE: 14.51%
MAE: 133894.58
==================================================
ARIMA(1, 1, 3)
MAPE: 14.0%
MAE: 130204.26
==================================================
ARIMA(1, 1, 4)
MAPE: 14.68%
MAE: 135665.11
==================================================
ARIMA(2, 0, 0)
MAPE: 9.58%
MAE: 94534.16
==================================================
ARIMA(2, 0, 1)
MAPE: 10.32%
MAE: 100729.52
==================================================
ARIMA(2, 0, 2)
MAPE: 12.03%
MAE: 115158.15
==================================================
ARIMA(2, 0, 3)
MAPE: 12.0%
MAE: 114690.77
==================================================
ARIMA(2, 0, 4)
MAPE: 14.48%
MAE: 137063.36
==================================================
ARIMA(2, 1, 0)
MAPE: 14.71%
MAE: 135618.89
==================================================
ARIMA(2, 1, 1)
MAPE: 14.46%
MAE: 134317.88
==================================================
ARIMA(2, 1, 2)
MAPE: 14.11%
MAE: 130608.15
==================================================
ARIMA(2, 1, 3)
MAPE: 17.92%
MAE: 166754.51
==================================================
ARIMA(2, 1, 4)
MAPE: 13.73%
MAE: 128365.36
==================================================
ARIMA(3, 0, 0)
MAPE: 11.24%
MAE: 108498.24
==================================================
ARIMA(3, 0, 1)
MAPE: 9.69%
MAE: 99254.55
==================================================
ARIMA(3, 0, 2)
MAPE: 13.68%
MAE: 129367.1
==================================================
ARIMA(3, 0, 3)
MAPE: 11.81%
MAE: 115207.52
==================================================
ARIMA(3, 0, 4)
MAPE: 12.33%
MAE: 120375.92
==================================================
ARIMA(3, 1, 0)
MAPE: 14.12%
MAE: 130716.15
==================================================
ARIMA(3, 1, 1)
MAPE: 13.81%
MAE: 128204.32
==================================================
ARIMA(3, 1, 2)
MAPE: 10.17%
MAE: 98047.86
==================================================
ARIMA(3, 1, 3)
MAPE: 17.87%
MAE: 166148.97
==================================================
ARIMA(3, 1, 4)
MAPE: 14.32%
MAE: 133078.61
==================================================
ARIMA(4, 0, 0)
MAPE: 11.67%
MAE: 111950.29
==================================================
ARIMA(4, 0, 1)
MAPE: 11.69%
MAE: 112258.54
==================================================
ARIMA(4, 0, 2)
MAPE: 11.7%
MAE: 114204.97
==================================================
ARIMA(4, 0, 3)
MAPE: 10.55%
MAE: 103426.77
==================================================
ARIMA(4, 0, 4)
MAPE: 13.64%
MAE: 132912.04
==================================================
ARIMA(4, 1, 0)
MAPE: 13.46%
MAE: 126548.25
==================================================
ARIMA(4, 1, 1)
MAPE: 14.7%
MAE: 137061.56
==================================================
ARIMA(4, 1, 2)
MAPE: 17.8%
MAE: 164956.22
==================================================
ARIMA(4, 1, 3)
MAPE: 20.34%
MAE: 187564.83
==================================================
ARIMA(4, 1, 4)
MAPE: 22.25%
MAE: 204748.32
==================================================

print(f"Best parameters model for KALIMANTAN TIMUR:", best_param_kalimantan_timur)
print(f"with the lowest MAPE: {lowest_mape_kalimantan_timur.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_kalimantan_timur.round(2)}")

Best parameters model for KALIMANTAN TIMUR: (2, 0, 0)
with the lowest MAPE: 9.58%
with the lowest MAE: 94534.16

plot_predictions_vs_actual(prediction_series=prediction_kalimantan_timur, actual_series=test_kalimantan_timur)

train_kalimantan_utara = training_data["KALIMANTAN UTARA"]
test_kalimantan_utara = test_data["KALIMANTAN UTARA"]

best_param_kalimantan_utara, lowest_mape_kalimantan_utara, lowest_mae_kalimantan_utara, prediction_kalimantan_utara = make_arima_prediction(train_kalimantan_utara, test_kalimantan_utara)

all_provinces_predictions['KALIMANTAN UTARA'] = prediction_kalimantan_utara

ARIMA(0, 0, 0)
MAPE: 34.6%
MAE: 27730.23
==================================================
ARIMA(0, 0, 1)
MAPE: 29.56%
MAE: 23508.88
==================================================
ARIMA(0, 0, 2)
MAPE: 25.98%
MAE: 20855.91
==================================================
ARIMA(0, 0, 3)
MAPE: 22.49%
MAE: 18335.55
==================================================
ARIMA(0, 0, 4)
MAPE: 19.87%
MAE: 15829.11
==================================================
ARIMA(0, 1, 0)
MAPE: 28.24%
MAE: 21795.34
==================================================
ARIMA(0, 1, 1)
MAPE: 10.88%
MAE: 8251.32
==================================================
ARIMA(0, 1, 2)
MAPE: 13.42%
MAE: 10064.55
==================================================
ARIMA(0, 1, 3)
MAPE: 12.65%
MAE: 9491.66
==================================================
ARIMA(0, 1, 4)
MAPE: 13.51%
MAE: 10154.44
==================================================
ARIMA(1, 0, 0)
MAPE: 16.7%
MAE: 12714.29
==================================================
ARIMA(1, 0, 1)
MAPE: 13.09%
MAE: 9949.54
==================================================
ARIMA(1, 0, 2)
MAPE: 13.65%
MAE: 10316.06
==================================================
ARIMA(1, 0, 3)
MAPE: 13.93%
MAE: 10508.63
==================================================
ARIMA(1, 0, 4)
MAPE: 14.43%
MAE: 10688.71
==================================================
ARIMA(1, 1, 0)
MAPE: 12.1%
MAE: 9091.7
==================================================
ARIMA(1, 1, 1)
MAPE: 12.77%
MAE: 9575.26
==================================================
ARIMA(1, 1, 2)
MAPE: 12.46%
MAE: 9346.83
==================================================
ARIMA(1, 1, 3)
MAPE: 12.4%
MAE: 9294.01
==================================================
ARIMA(1, 1, 4)
MAPE: 13.99%
MAE: 10504.24
==================================================
ARIMA(2, 0, 0)
MAPE: 12.82%
MAE: 9724.65
==================================================
ARIMA(2, 0, 1)
MAPE: 13.36%
MAE: 10100.55
==================================================
ARIMA(2, 0, 2)
MAPE: 13.13%
MAE: 9933.06
==================================================
ARIMA(2, 0, 3)
MAPE: 13.21%
MAE: 9945.95
==================================================
ARIMA(2, 0, 4)
MAPE: 14.85%
MAE: 10950.92
==================================================
ARIMA(2, 1, 0)
MAPE: 12.77%
MAE: 9574.16
==================================================
ARIMA(2, 1, 1)
MAPE: 7.31%
MAE: 5699.95
==================================================
ARIMA(2, 1, 2)
MAPE: 12.48%
MAE: 9360.03
==================================================
ARIMA(2, 1, 3)
MAPE: 13.46%
MAE: 10104.12
==================================================
ARIMA(2, 1, 4)
MAPE: 11.9%
MAE: 8912.78
==================================================
ARIMA(3, 0, 0)
MAPE: 13.79%
MAE: 10419.95
==================================================
ARIMA(3, 0, 1)
MAPE: 13.21%
MAE: 9986.08
==================================================
ARIMA(3, 0, 2)
MAPE: 13.21%
MAE: 9988.43
==================================================
ARIMA(3, 0, 3)
MAPE: 13.03%
MAE: 9821.69
==================================================
ARIMA(3, 0, 4)
MAPE: 16.04%
MAE: 11934.04
==================================================
ARIMA(3, 1, 0)
MAPE: 12.58%
MAE: 9434.52
==================================================
ARIMA(3, 1, 1)
MAPE: 12.71%
MAE: 9531.35
==================================================
ARIMA(3, 1, 2)
MAPE: 13.36%
MAE: 10025.32
==================================================
ARIMA(3, 1, 3)
MAPE: 9.9%
MAE: 7476.68
==================================================
ARIMA(3, 1, 4)
MAPE: 16.67%
MAE: 12530.85
==================================================
ARIMA(4, 0, 0)
MAPE: 14.05%
MAE: 10600.32
==================================================
ARIMA(4, 0, 1)
MAPE: 13.46%
MAE: 10173.51
==================================================
ARIMA(4, 0, 2)
MAPE: 13.55%
MAE: 10180.15
==================================================
ARIMA(4, 0, 3)
MAPE: 20.16%
MAE: 15200.83
==================================================
ARIMA(4, 0, 4)
MAPE: 14.43%
MAE: 10810.59
==================================================
ARIMA(4, 1, 0)
MAPE: 13.38%
MAE: 10056.12
==================================================
ARIMA(4, 1, 1)
MAPE: 13.82%
MAE: 10386.14
==================================================
ARIMA(4, 1, 2)
MAPE: 18.09%
MAE: 13661.68
==================================================
ARIMA(4, 1, 3)
MAPE: 6.67%
MAE: 5314.59
==================================================
ARIMA(4, 1, 4)
MAPE: 22.74%
MAE: 17333.26
==================================================

print(f"Best parameters model for KALIMANTAN UTARA:", best_param_kalimantan_utara)
print(f"with the lowest MAPE: {lowest_mape_kalimantan_utara.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_kalimantan_utara.round(2)}")

Best parameters model for KALIMANTAN UTARA: (4, 1, 3)
with the lowest MAPE: 6.67%
with the lowest MAE: 5314.59

plot_predictions_vs_actual(prediction_series=prediction_kalimantan_utara, actual_series=test_kalimantan_utara)

train_sulawesi_utara = training_data["SULAWESI UTARA"]
test_sulawesi_utara = test_data["SULAWESI UTARA"]

best_param_sulawesi_utara, lowest_mape_sulawesi_utara, lowest_mae_sulawesi_utara, prediction_sulawesi_utara = make_arima_prediction(train_sulawesi_utara, test_sulawesi_utara)

all_provinces_predictions['SULAWESI UTARA'] = prediction_sulawesi_utara

ARIMA(0, 0, 0)
MAPE: 46.09%
MAE: 312112.71
==================================================
ARIMA(0, 0, 1)
MAPE: 42.56%
MAE: 287236.94
==================================================
ARIMA(0, 0, 2)
MAPE: 38.43%
MAE: 261786.85
==================================================
ARIMA(0, 0, 3)
MAPE: 34.17%
MAE: 234157.77
==================================================
ARIMA(0, 0, 4)
MAPE: 30.01%
MAE: 205592.87
==================================================
ARIMA(0, 1, 0)
MAPE: 7.41%
MAE: 49020.58
==================================================
ARIMA(0, 1, 1)
MAPE: 7.4%
MAE: 52457.28
==================================================
ARIMA(0, 1, 2)
MAPE: 5.54%
MAE: 39881.49
==================================================
ARIMA(0, 1, 3)
MAPE: 10.1%
MAE: 71008.21
==================================================
ARIMA(0, 1, 4)
MAPE: 12.71%
MAE: 89325.72
==================================================
ARIMA(1, 0, 0)
MAPE: 20.15%
MAE: 138124.29
==================================================
ARIMA(1, 0, 1)
MAPE: 19.61%
MAE: 134442.43
==================================================
ARIMA(1, 0, 2)
MAPE: 19.81%
MAE: 136178.23
==================================================
ARIMA(1, 0, 3)
MAPE: 19.85%
MAE: 136385.27
==================================================
ARIMA(1, 0, 4)
MAPE: 19.81%
MAE: 136770.98
==================================================
ARIMA(1, 1, 0)
MAPE: 5.84%
MAE: 42011.17
==================================================
ARIMA(1, 1, 1)
MAPE: 5.83%
MAE: 41896.46
==================================================
ARIMA(1, 1, 2)
MAPE: 5.75%
MAE: 41230.83
==================================================
ARIMA(1, 1, 3)
MAPE: 13.01%
MAE: 91104.94
==================================================
ARIMA(1, 1, 4)
MAPE: 10.8%
MAE: 76858.48
==================================================
ARIMA(2, 0, 0)
MAPE: 19.52%
MAE: 133827.03
==================================================
ARIMA(2, 0, 1)
MAPE: 19.57%
MAE: 134248.33
==================================================
ARIMA(2, 0, 2)
MAPE: 19.75%
MAE: 135747.38
==================================================
ARIMA(2, 0, 3)
MAPE: 19.81%
MAE: 136409.18
==================================================
ARIMA(2, 0, 4)
MAPE: 19.78%
MAE: 136693.28
==================================================
ARIMA(2, 1, 0)
MAPE: 5.81%
MAE: 41802.26
==================================================
ARIMA(2, 1, 1)
MAPE: 12.56%
MAE: 87422.33
==================================================
ARIMA(2, 1, 2)
MAPE: 12.55%
MAE: 87419.18
==================================================
ARIMA(2, 1, 3)
MAPE: 13.08%
MAE: 91569.11
==================================================
ARIMA(2, 1, 4)
MAPE: 12.77%
MAE: 90007.64
==================================================
ARIMA(3, 0, 0)
MAPE: 19.53%
MAE: 134062.49
==================================================
ARIMA(3, 0, 1)
MAPE: 19.58%
MAE: 134389.68
==================================================
ARIMA(3, 0, 2)
MAPE: 20.39%
MAE: 140460.76
==================================================
ARIMA(3, 0, 3)
MAPE: 21.91%
MAE: 150580.89
==================================================
ARIMA(3, 0, 4)
MAPE: 19.75%
MAE: 136308.47
==================================================
ARIMA(3, 1, 0)
MAPE: 5.74%
MAE: 41188.77
==================================================
ARIMA(3, 1, 1)
MAPE: 12.7%
MAE: 88551.18
==================================================
ARIMA(3, 1, 2)
MAPE: 20.7%
MAE: 142511.5
==================================================
ARIMA(3, 1, 3)
MAPE: 11.54%
MAE: 81196.42
==================================================
ARIMA(3, 1, 4)
MAPE: 10.84%
MAE: 76399.91
==================================================
ARIMA(4, 0, 0)
MAPE: 19.6%
MAE: 134569.19
==================================================
ARIMA(4, 0, 1)
MAPE: 19.7%
MAE: 135463.68
==================================================
ARIMA(4, 0, 2)
MAPE: 20.26%
MAE: 139567.77
==================================================
ARIMA(4, 0, 3)
MAPE: 20.14%
MAE: 138998.18
==================================================
ARIMA(4, 0, 4)
MAPE: 21.4%
MAE: 147273.87
==================================================
ARIMA(4, 1, 0)
MAPE: 9.18%
MAE: 65572.59
==================================================
ARIMA(4, 1, 1)
MAPE: 9.46%
MAE: 67484.85
==================================================
ARIMA(4, 1, 2)
MAPE: 9.13%
MAE: 65272.09
==================================================
ARIMA(4, 1, 3)
MAPE: 11.2%
MAE: 78795.09
==================================================
ARIMA(4, 1, 4)
MAPE: 7.52%
MAE: 53833.82
==================================================

print(f"Best parameters model for SULAWESI UTARA:", best_param_sulawesi_utara)
print(f"with the lowest MAPE: {lowest_mape_sulawesi_utara.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_sulawesi_utara.round(2)}")

Best parameters model for SULAWESI UTARA: (0, 1, 2)
with the lowest MAPE: 5.54%
with the lowest MAE: 39881.49

plot_predictions_vs_actual(prediction_series=prediction_sulawesi_utara, actual_series=test_sulawesi_utara)

train_sulawesi_tengah = training_data["SULAWESI TENGAH"]
test_sulawesi_tengah = test_data["SULAWESI TENGAH"]

best_param_sulawesi_tengah, lowest_mape_sulawesi_tengah, lowest_mae_sulawesi_tengah, prediction_sulawesi_tengah = make_arima_prediction(train_sulawesi_tengah, test_sulawesi_tengah)

all_provinces_predictions['SULAWESI TENGAH'] = prediction_sulawesi_tengah

ARIMA(0, 0, 0)
MAPE: 54.06%
MAE: 475493.73
==================================================
ARIMA(0, 0, 1)
MAPE: 45.13%
MAE: 392386.93
==================================================
ARIMA(0, 0, 2)
MAPE: 35.44%
MAE: 310463.85
==================================================
ARIMA(0, 0, 3)
MAPE: 34.16%
MAE: 301609.59
==================================================
ARIMA(0, 0, 4)
MAPE: 27.2%
MAE: 233919.71
==================================================
ARIMA(0, 1, 0)
MAPE: 17.66%
MAE: 145961.5
==================================================
ARIMA(0, 1, 1)
MAPE: 13.04%
MAE: 109561.26
==================================================
ARIMA(0, 1, 2)
MAPE: 21.8%
MAE: 176501.38
==================================================
ARIMA(0, 1, 3)
MAPE: 9.91%
MAE: 92763.33
==================================================
ARIMA(0, 1, 4)
MAPE: 9.87%
MAE: 92466.88
==================================================
ARIMA(1, 0, 0)
MAPE: 11.26%
MAE: 102400.55
==================================================
ARIMA(1, 0, 1)
MAPE: 11.78%
MAE: 106908.77
==================================================
ARIMA(1, 0, 2)
MAPE: 15.95%
MAE: 140550.97
==================================================
ARIMA(1, 0, 3)
MAPE: 15.88%
MAE: 141959.67
==================================================
ARIMA(1, 0, 4)
MAPE: 15.91%
MAE: 134319.78
==================================================
ARIMA(1, 1, 0)
MAPE: 12.53%
MAE: 105887.9
==================================================
ARIMA(1, 1, 1)
MAPE: 13.47%
MAE: 113251.85
==================================================
ARIMA(1, 1, 2)
MAPE: 20.2%
MAE: 163498.29
==================================================
ARIMA(1, 1, 3)
MAPE: 9.92%
MAE: 89722.52
==================================================
ARIMA(1, 1, 4)
MAPE: 10.03%
MAE: 90383.86
==================================================
ARIMA(2, 0, 0)
MAPE: 12.62%
MAE: 113932.28
==================================================
ARIMA(2, 0, 1)
MAPE: 11.9%
MAE: 108216.64
==================================================
ARIMA(2, 0, 2)
MAPE: 15.37%
MAE: 135813.31
==================================================
ARIMA(2, 0, 3)
MAPE: 15.34%
MAE: 135802.28
==================================================
ARIMA(2, 0, 4)
MAPE: 17.25%
MAE: 145131.37
==================================================
ARIMA(2, 1, 0)
MAPE: 19.77%
MAE: 159401.25
==================================================
ARIMA(2, 1, 1)
MAPE: 19.08%
MAE: 154354.93
==================================================
ARIMA(2, 1, 2)
MAPE: 14.41%
MAE: 120623.51
==================================================
ARIMA(2, 1, 3)
MAPE: 9.43%
MAE: 85834.74
==================================================
ARIMA(2, 1, 4)
MAPE: 9.05%
MAE: 82997.99
==================================================
ARIMA(3, 0, 0)
MAPE: 15.03%
MAE: 130491.13
==================================================
ARIMA(3, 0, 1)
MAPE: 15.56%
MAE: 135759.22
==================================================
ARIMA(3, 0, 2)
MAPE: 14.91%
MAE: 131101.92
==================================================
ARIMA(3, 0, 3)
MAPE: 15.09%
MAE: 133420.55
==================================================
ARIMA(3, 0, 4)
MAPE: 15.15%
MAE: 128491.68
==================================================
ARIMA(3, 1, 0)
MAPE: 15.81%
MAE: 131181.37
==================================================
ARIMA(3, 1, 1)
MAPE: 11.64%
MAE: 102070.78
==================================================
ARIMA(3, 1, 2)
MAPE: 14.21%
MAE: 118013.74
==================================================
ARIMA(3, 1, 3)
MAPE: 15.76%
MAE: 128876.41
==================================================
ARIMA(3, 1, 4)
MAPE: 15.35%
MAE: 125938.21
==================================================
ARIMA(4, 0, 0)
MAPE: 16.31%
MAE: 142509.83
==================================================
ARIMA(4, 0, 1)
MAPE: 15.67%
MAE: 136883.49
==================================================
ARIMA(4, 0, 2)
MAPE: 15.18%
MAE: 133873.56
==================================================
ARIMA(4, 0, 3)
MAPE: 14.76%
MAE: 129174.43
==================================================
ARIMA(4, 0, 4)
MAPE: 18.6%
MAE: 160272.46
==================================================
ARIMA(4, 1, 0)
MAPE: 13.69%
MAE: 117091.12
==================================================
ARIMA(4, 1, 1)
MAPE: 12.15%
MAE: 105938.49
==================================================
ARIMA(4, 1, 2)
MAPE: 12.11%
MAE: 105755.75
==================================================
ARIMA(4, 1, 3)
MAPE: 12.9%
MAE: 109436.04
==================================================
ARIMA(4, 1, 4)
MAPE: 15.43%
MAE: 134555.84
==================================================

print(f"Best parameters model for SULAWESI TENGAH:", best_param_sulawesi_tengah)
print(f"with the lowest MAPE: {lowest_mape_sulawesi_tengah.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_sulawesi_tengah.round(2)}")

Best parameters model for SULAWESI TENGAH: (2, 1, 4)
with the lowest MAPE: 9.05%
with the lowest MAE: 82997.99

plot_predictions_vs_actual(prediction_series=prediction_sulawesi_tengah, actual_series=test_sulawesi_tengah)

train_sulawesi_selatan = training_data["SULAWESI SELATAN"]
test_sulawesi_selatan = test_data["SULAWESI SELATAN"]

best_param_sulawesi_selatan, lowest_mape_sulawesi_selatan, lowest_mae_sulawesi_selatan, prediction_sulawesi_selatan = make_arima_prediction(train_sulawesi_selatan, test_sulawesi_selatan)

all_provinces_predictions['SULAWESI SELATAN'] = prediction_sulawesi_selatan

ARIMA(0, 0, 0)
MAPE: 46.44%
MAE: 1484791.42
==================================================
ARIMA(0, 0, 1)
MAPE: 38.47%
MAE: 1217817.99
==================================================
ARIMA(0, 0, 2)
MAPE: 32.2%
MAE: 1028406.38
==================================================
ARIMA(0, 0, 3)
MAPE: 29.76%
MAE: 956893.89
==================================================
ARIMA(0, 0, 4)
MAPE: 28.62%
MAE: 917585.24
==================================================
ARIMA(0, 1, 0)
MAPE: 23.3%
MAE: 722611.19
==================================================
ARIMA(0, 1, 1)
MAPE: 13.19%
MAE: 402623.57
==================================================
ARIMA(0, 1, 2)
MAPE: 30.42%
MAE: 942935.08
==================================================
ARIMA(0, 1, 3)
MAPE: 12.49%
MAE: 399933.7
==================================================
ARIMA(0, 1, 4)
MAPE: 15.05%
MAE: 485868.76
==================================================
ARIMA(1, 0, 0)
MAPE: 14.29%
MAE: 465770.1
==================================================
ARIMA(1, 0, 1)
MAPE: 14.61%
MAE: 476088.83
==================================================
ARIMA(1, 0, 2)
MAPE: 23.62%
MAE: 759941.39
==================================================
ARIMA(1, 0, 3)
MAPE: 20.75%
MAE: 664635.92
==================================================
ARIMA(1, 0, 4)
MAPE: 21.42%
MAE: 687767.2
==================================================
ARIMA(1, 1, 0)
MAPE: 12.33%
MAE: 374633.6
==================================================
ARIMA(1, 1, 1)
MAPE: 14.77%
MAE: 451579.25
==================================================
ARIMA(1, 1, 2)
MAPE: 31.37%
MAE: 973225.05
==================================================
ARIMA(1, 1, 3)
MAPE: 17.05%
MAE: 543210.5
==================================================
ARIMA(1, 1, 4)
MAPE: 14.59%
MAE: 464989.77
==================================================
ARIMA(2, 0, 0)
MAPE: 14.76%
MAE: 480831.04
==================================================
ARIMA(2, 0, 1)
MAPE: 13.53%
MAE: 438176.35
==================================================
ARIMA(2, 0, 2)
MAPE: 21.83%
MAE: 699920.42
==================================================
ARIMA(2, 0, 3)
MAPE: 21.41%
MAE: 686774.63
==================================================
ARIMA(2, 0, 4)
MAPE: 22.46%
MAE: 719184.04
==================================================
ARIMA(2, 1, 0)
MAPE: 22.83%
MAE: 704395.24
==================================================
ARIMA(2, 1, 1)
MAPE: 22.75%
MAE: 702697.16
==================================================
ARIMA(2, 1, 2)
MAPE: 9.71%
MAE: 313958.72
==================================================
ARIMA(2, 1, 3)
MAPE: 15.46%
MAE: 490155.86
==================================================
ARIMA(2, 1, 4)
MAPE: 14.4%
MAE: 457989.32
==================================================
ARIMA(3, 0, 0)
MAPE: 19.27%
MAE: 613813.87
==================================================
ARIMA(3, 0, 1)
MAPE: 19.38%
MAE: 618928.77
==================================================
ARIMA(3, 0, 2)
MAPE: 19.64%
MAE: 631732.15
==================================================
ARIMA(3, 0, 3)
MAPE: 19.8%
MAE: 637767.62
==================================================
ARIMA(3, 0, 4)
MAPE: 21.38%
MAE: 685727.81
==================================================
ARIMA(3, 1, 0)
MAPE: 14.84%
MAE: 454578.43
==================================================
ARIMA(3, 1, 1)
MAPE: 14.82%
MAE: 461957.72
==================================================
ARIMA(3, 1, 2)
MAPE: 14.75%
MAE: 460437.13
==================================================
ARIMA(3, 1, 3)
MAPE: 12.37%
MAE: 395737.92
==================================================
ARIMA(3, 1, 4)
MAPE: 14.11%
MAE: 447545.21
==================================================
ARIMA(4, 0, 0)
MAPE: 19.83%
MAE: 636842.64
==================================================
ARIMA(4, 0, 1)
MAPE: 21.62%
MAE: 694328.29
==================================================
ARIMA(4, 0, 2)
MAPE: 19.69%
MAE: 634400.56
==================================================
ARIMA(4, 0, 3)
MAPE: 19.79%
MAE: 637601.14
==================================================
ARIMA(4, 0, 4)
MAPE: 22.62%
MAE: 726920.77
==================================================
ARIMA(4, 1, 0)
MAPE: 13.37%
MAE: 417651.2
==================================================
ARIMA(4, 1, 1)
MAPE: 12.0%
MAE: 373768.76
==================================================
ARIMA(4, 1, 2)
MAPE: 14.04%
MAE: 434758.04
==================================================
ARIMA(4, 1, 3)
MAPE: 17.4%
MAE: 563992.98
==================================================
ARIMA(4, 1, 4)
MAPE: 14.75%
MAE: 469925.01
==================================================

print(f"Best parameters model for SULAWESI SELATAN:", best_param_sulawesi_selatan)
print(f"with the lowest MAPE: {lowest_mape_sulawesi_selatan.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_sulawesi_selatan.round(2)}")

Best parameters model for SULAWESI SELATAN: (2, 1, 2)
with the lowest MAPE: 9.71%
with the lowest MAE: 313958.72

plot_predictions_vs_actual(prediction_series=prediction_sulawesi_selatan, actual_series=test_sulawesi_selatan)

train_sulawesi_tenggara = training_data["SULAWESI TENGGARA"]
test_sulawesi_tenggara = test_data["SULAWESI TENGGARA"]

best_param_sulawesi_tenggara, lowest_mape_sulawesi_tenggara, lowest_mae_sulawesi_tenggara, prediction_sulawesi_tenggara = make_arima_prediction(train_sulawesi_tenggara, test_sulawesi_tenggara)

all_provinces_predictions['SULAWESI TENGGARA'] = prediction_sulawesi_tenggara

ARIMA(0, 0, 0)
MAPE: 40.3%
MAE: 478652.57
==================================================
ARIMA(0, 0, 1)
MAPE: 34.73%
MAE: 412182.15
==================================================
ARIMA(0, 0, 2)
MAPE: 32.32%
MAE: 384153.81
==================================================
ARIMA(0, 0, 3)
MAPE: 29.35%
MAE: 352469.37
==================================================
ARIMA(0, 0, 4)
MAPE: 23.98%
MAE: 287048.0
==================================================
ARIMA(0, 1, 0)
MAPE: 7.95%
MAE: 102200.0
==================================================
ARIMA(0, 1, 1)
MAPE: 9.68%
MAE: 124690.52
==================================================
ARIMA(0, 1, 2)
MAPE: 8.48%
MAE: 108901.42
==================================================
ARIMA(0, 1, 3)
MAPE: 17.12%
MAE: 210517.66
==================================================
ARIMA(0, 1, 4)
MAPE: 16.65%
MAE: 205356.31
==================================================
ARIMA(1, 0, 0)
MAPE: 19.13%
MAE: 233216.82
==================================================
ARIMA(1, 0, 1)
MAPE: 18.7%
MAE: 228223.34
==================================================
ARIMA(1, 0, 2)
MAPE: 16.36%
MAE: 201334.34
==================================================
ARIMA(1, 0, 3)
MAPE: 20.39%
MAE: 247618.37
==================================================
ARIMA(1, 0, 4)
MAPE: 18.88%
MAE: 229521.23
==================================================
ARIMA(1, 1, 0)
MAPE: 9.77%
MAE: 125756.82
==================================================
ARIMA(1, 1, 1)
MAPE: 8.14%
MAE: 106134.87
==================================================
ARIMA(1, 1, 2)
MAPE: 6.82%
MAE: 88854.61
==================================================
ARIMA(1, 1, 3)
MAPE: 9.77%
MAE: 124216.08
==================================================
ARIMA(1, 1, 4)
MAPE: 11.15%
MAE: 141824.7
==================================================
ARIMA(2, 0, 0)
MAPE: 18.45%
MAE: 225363.47
==================================================
ARIMA(2, 0, 1)
MAPE: 19.95%
MAE: 242372.06
==================================================
ARIMA(2, 0, 2)
MAPE: 16.57%
MAE: 202288.02
==================================================
ARIMA(2, 0, 3)
MAPE: 16.65%
MAE: 203313.49
==================================================
ARIMA(2, 0, 4)
MAPE: 18.09%
MAE: 220138.5
==================================================
ARIMA(2, 1, 0)
MAPE: 8.54%
MAE: 109884.83
==================================================
ARIMA(2, 1, 1)
MAPE: 6.95%
MAE: 90610.85
==================================================
ARIMA(2, 1, 2)
MAPE: 13.73%
MAE: 171151.17
==================================================
ARIMA(2, 1, 3)
MAPE: 9.07%
MAE: 116821.69
==================================================
ARIMA(2, 1, 4)
MAPE: 8.86%
MAE: 113703.42
==================================================
ARIMA(3, 0, 0)
MAPE: 16.23%
MAE: 199756.36
==================================================
ARIMA(3, 0, 1)
MAPE: 15.99%
MAE: 195876.74
==================================================
ARIMA(3, 0, 2)
MAPE: 16.53%
MAE: 201920.23
==================================================
ARIMA(3, 0, 3)
MAPE: 17.54%
MAE: 213438.34
==================================================
ARIMA(3, 0, 4)
MAPE: 17.98%
MAE: 218947.39
==================================================
ARIMA(3, 1, 0)
MAPE: 13.57%
MAE: 169366.68
==================================================
ARIMA(3, 1, 1)
MAPE: 10.14%
MAE: 126873.48
==================================================
ARIMA(3, 1, 2)
MAPE: 8.78%
MAE: 112202.47
==================================================
ARIMA(3, 1, 3)
MAPE: 16.86%
MAE: 206348.45
==================================================
ARIMA(3, 1, 4)
MAPE: 8.92%
MAE: 114886.43
==================================================
ARIMA(4, 0, 0)
MAPE: 20.09%
MAE: 243992.65
==================================================
ARIMA(4, 0, 1)
MAPE: 17.05%
MAE: 207916.97
==================================================
ARIMA(4, 0, 2)
MAPE: 17.74%
MAE: 216029.32
==================================================
ARIMA(4, 0, 3)
MAPE: 17.69%
MAE: 215538.52
==================================================
ARIMA(4, 0, 4)
MAPE: 18.25%
MAE: 221550.38
==================================================
ARIMA(4, 1, 0)
MAPE: 14.4%
MAE: 179120.48
==================================================
ARIMA(4, 1, 1)
MAPE: 9.9%
MAE: 126974.63
==================================================
ARIMA(4, 1, 2)
MAPE: 8.77%
MAE: 112014.05
==================================================
ARIMA(4, 1, 3)
MAPE: 8.79%
MAE: 112304.27
==================================================
ARIMA(4, 1, 4)
MAPE: 14.28%
MAE: 177276.68
==================================================

print(f"Best parameters model for SULAWESI TENGGARA:", best_param_sulawesi_tenggara)
print(f"with the lowest MAPE: {lowest_mape_sulawesi_tenggara.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_sulawesi_tenggara.round(2)}")

Best parameters model for SULAWESI TENGGARA: (1, 1, 2)
with the lowest MAPE: 6.82%
with the lowest MAE: 88854.61

plot_predictions_vs_actual(prediction_series=prediction_sulawesi_tenggara, actual_series=test_sulawesi_tenggara)

train_gorontalo = training_data["GORONTALO"]
test_gorontalo = test_data["GORONTALO"]

best_param_gorontalo, lowest_mape_gorontalo, lowest_mae_gorontalo, prediction_gorontalo = make_arima_prediction(train_gorontalo, test_gorontalo)

all_provinces_predictions['GORONTALO'] = prediction_gorontalo

ARIMA(0, 0, 0)
MAPE: 58.64%
MAE: 175163.08
==================================================
ARIMA(0, 0, 1)
MAPE: 51.81%
MAE: 155691.84
==================================================
ARIMA(0, 0, 2)
MAPE: 41.43%
MAE: 127395.85
==================================================
ARIMA(0, 0, 3)
MAPE: 36.81%
MAE: 114531.42
==================================================
ARIMA(0, 0, 4)
MAPE: 35.69%
MAE: 110034.54
==================================================
ARIMA(0, 1, 0)
MAPE: 22.16%
MAE: 62909.35
==================================================
ARIMA(0, 1, 1)
MAPE: 5.46%
MAE: 18933.45
==================================================
ARIMA(0, 1, 2)
MAPE: 12.97%
MAE: 38313.8
==================================================
ARIMA(0, 1, 3)
MAPE: 10.18%
MAE: 32632.09
==================================================
ARIMA(0, 1, 4)
MAPE: 12.54%
MAE: 37313.94
==================================================
ARIMA(1, 0, 0)
MAPE: 24.98%
MAE: 77617.86
==================================================
ARIMA(1, 0, 1)
MAPE: 22.49%
MAE: 70008.9
==================================================
ARIMA(1, 0, 2)
MAPE: 21.61%
MAE: 67812.88
==================================================
ARIMA(1, 0, 3)
MAPE: 23.48%
MAE: 73205.12
==================================================
ARIMA(1, 0, 4)
MAPE: 26.12%
MAE: 79483.68
==================================================
ARIMA(1, 1, 0)
MAPE: 7.57%
MAE: 24559.63
==================================================
ARIMA(1, 1, 1)
MAPE: 8.76%
MAE: 27437.29
==================================================
ARIMA(1, 1, 2)
MAPE: 12.33%
MAE: 36667.6
==================================================
ARIMA(1, 1, 3)
MAPE: 12.25%
MAE: 39103.65
==================================================
ARIMA(1, 1, 4)
MAPE: 16.4%
MAE: 52392.08
==================================================
ARIMA(2, 0, 0)
MAPE: 22.05%
MAE: 68639.42
==================================================
ARIMA(2, 0, 1)
MAPE: 21.42%
MAE: 66818.83
==================================================
ARIMA(2, 0, 2)
MAPE: 22.25%
MAE: 69449.84
==================================================
ARIMA(2, 0, 3)
MAPE: 23.77%
MAE: 73915.66
==================================================
ARIMA(2, 0, 4)
MAPE: 39.22%
MAE: 122146.37
==================================================
ARIMA(2, 1, 0)
MAPE: 10.07%
MAE: 30689.15
==================================================
ARIMA(2, 1, 1)
MAPE: 11.05%
MAE: 33231.62
==================================================
ARIMA(2, 1, 2)
MAPE: 21.56%
MAE: 62172.04
==================================================
ARIMA(2, 1, 3)
MAPE: 12.38%
MAE: 37957.0
==================================================
ARIMA(2, 1, 4)
MAPE: 14.08%
MAE: 41535.01
==================================================
ARIMA(3, 0, 0)
MAPE: 20.86%
MAE: 65256.6
==================================================
ARIMA(3, 0, 1)
MAPE: 20.77%
MAE: 64978.77
==================================================
ARIMA(3, 0, 2)
MAPE: 21.18%
MAE: 67240.87
==================================================
ARIMA(3, 0, 3)
MAPE: 21.53%
MAE: 68230.54
==================================================
ARIMA(3, 0, 4)
MAPE: 21.34%
MAE: 67622.89
==================================================
ARIMA(3, 1, 0)
MAPE: 12.89%
MAE: 38257.18
==================================================
ARIMA(3, 1, 1)
MAPE: 13.13%
MAE: 39053.61
==================================================
ARIMA(3, 1, 2)
MAPE: 10.13%
MAE: 33745.59
==================================================
ARIMA(3, 1, 3)
MAPE: 13.06%
MAE: 42075.69
==================================================
ARIMA(3, 1, 4)
MAPE: 11.58%
MAE: 37271.26
==================================================
ARIMA(4, 0, 0)
MAPE: 20.8%
MAE: 65176.44
==================================================
ARIMA(4, 0, 1)
MAPE: 21.26%
MAE: 66662.85
==================================================
ARIMA(4, 0, 2)
MAPE: 21.11%
MAE: 66946.49
==================================================
ARIMA(4, 0, 3)
MAPE: 21.44%
MAE: 68028.83
==================================================
ARIMA(4, 0, 4)
MAPE: 21.36%
MAE: 67587.34
==================================================
ARIMA(4, 1, 0)
MAPE: 11.06%
MAE: 34225.25
==================================================
ARIMA(4, 1, 1)
MAPE: 11.38%
MAE: 35029.17
==================================================
ARIMA(4, 1, 2)
MAPE: 13.93%
MAE: 44187.33
==================================================
ARIMA(4, 1, 3)
MAPE: 11.56%
MAE: 36954.54
==================================================
ARIMA(4, 1, 4)
MAPE: 20.6%
MAE: 58764.19
==================================================

print(f"Best parameters model for GORONTALO:", best_param_gorontalo)
print(f"with the lowest MAPE: {lowest_mape_gorontalo.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_gorontalo.round(2)}")

Best parameters model for GORONTALO: (0, 1, 1)
with the lowest MAPE: 5.46%
with the lowest MAE: 18933.45

plot_predictions_vs_actual(prediction_series=prediction_gorontalo, actual_series=test_gorontalo)

train_sulawesi_barat = training_data["SULAWESI BARAT"]
test_sulawesi_barat = test_data["SULAWESI BARAT"]

best_param_sulawesi_barat, lowest_mape_sulawesi_barat, lowest_mae_sulawesi_barat, prediction_sulawesi_barat = make_arima_prediction(train_sulawesi_barat, test_sulawesi_barat)

all_provinces_predictions['SULAWESI BARAT'] = prediction_sulawesi_barat

ARIMA(0, 0, 0)
MAPE: 38.18%
MAE: 142811.41
==================================================
ARIMA(0, 0, 1)
MAPE: 30.54%
MAE: 113420.02
==================================================
ARIMA(0, 0, 2)
MAPE: 26.39%
MAE: 98705.66
==================================================
ARIMA(0, 0, 3)
MAPE: 23.03%
MAE: 87092.88
==================================================
ARIMA(0, 0, 4)
MAPE: 21.5%
MAE: 80483.53
==================================================
ARIMA(0, 1, 0)
MAPE: 10.33%
MAE: 37554.27
==================================================
ARIMA(0, 1, 1)
MAPE: 10.34%
MAE: 37565.1
==================================================
ARIMA(0, 1, 2)
MAPE: 25.74%
MAE: 88933.65
==================================================
ARIMA(0, 1, 3)
MAPE: 9.41%
MAE: 37205.0
==================================================
ARIMA(0, 1, 4)
MAPE: 10.43%
MAE: 40814.82
==================================================
ARIMA(1, 0, 0)
MAPE: 11.57%
MAE: 44825.42
==================================================
ARIMA(1, 0, 1)
MAPE: 10.42%
MAE: 39967.94
==================================================
ARIMA(1, 0, 2)
MAPE: 17.32%
MAE: 63893.24
==================================================
ARIMA(1, 0, 3)
MAPE: 14.47%
MAE: 54390.43
==================================================
ARIMA(1, 0, 4)
MAPE: 14.51%
MAE: 53987.06
==================================================
ARIMA(1, 1, 0)
MAPE: 10.35%
MAE: 37583.63
==================================================
ARIMA(1, 1, 1)
MAPE: 10.36%
MAE: 37607.99
==================================================
ARIMA(1, 1, 2)
MAPE: 20.69%
MAE: 71103.4
==================================================
ARIMA(1, 1, 3)
MAPE: 12.3%
MAE: 44943.13
==================================================
ARIMA(1, 1, 4)
MAPE: 10.55%
MAE: 39603.84
==================================================
ARIMA(2, 0, 0)
MAPE: 11.78%
MAE: 44592.5
==================================================
ARIMA(2, 0, 1)
MAPE: 11.53%
MAE: 44801.89
==================================================
ARIMA(2, 0, 2)
MAPE: 14.67%
MAE: 54592.66
==================================================
ARIMA(2, 0, 3)
MAPE: 14.46%
MAE: 54122.06
==================================================
ARIMA(2, 0, 4)
MAPE: 14.57%
MAE: 54773.01
==================================================
ARIMA(2, 1, 0)
MAPE: 18.12%
MAE: 62192.17
==================================================
ARIMA(2, 1, 1)
MAPE: 17.79%
MAE: 60908.63
==================================================
ARIMA(2, 1, 2)
MAPE: 14.22%
MAE: 52031.69
==================================================
ARIMA(2, 1, 3)
MAPE: 10.67%
MAE: 39561.23
==================================================
ARIMA(2, 1, 4)
MAPE: 10.49%
MAE: 39150.8
==================================================
ARIMA(3, 0, 0)
MAPE: 13.11%
MAE: 47458.3
==================================================
ARIMA(3, 0, 1)
MAPE: 14.13%
MAE: 51411.47
==================================================
ARIMA(3, 0, 2)
MAPE: 14.23%
MAE: 53391.51
==================================================
ARIMA(3, 0, 3)
MAPE: 14.46%
MAE: 54325.84
==================================================
ARIMA(3, 0, 4)
MAPE: 14.71%
MAE: 55093.81
==================================================
ARIMA(3, 1, 0)
MAPE: 11.38%
MAE: 41174.81
==================================================
ARIMA(3, 1, 1)
MAPE: 13.57%
MAE: 47680.04
==================================================
ARIMA(3, 1, 2)
MAPE: 14.07%
MAE: 49034.62
==================================================
ARIMA(3, 1, 3)
MAPE: 12.48%
MAE: 47944.69
==================================================
ARIMA(3, 1, 4)
MAPE: 14.24%
MAE: 53670.19
==================================================
ARIMA(4, 0, 0)
MAPE: 16.37%
MAE: 60386.43
==================================================
ARIMA(4, 0, 1)
MAPE: 17.4%
MAE: 64084.93
==================================================
ARIMA(4, 0, 2)
MAPE: 18.28%
MAE: 66929.57
==================================================
ARIMA(4, 0, 3)
MAPE: 15.83%
MAE: 59127.2
==================================================
ARIMA(4, 0, 4)
MAPE: 15.29%
MAE: 57378.98
==================================================
ARIMA(4, 1, 0)
MAPE: 12.3%
MAE: 45570.31
==================================================
ARIMA(4, 1, 1)
MAPE: 11.47%
MAE: 43282.03
==================================================
ARIMA(4, 1, 2)
MAPE: 12.63%
MAE: 44884.55
==================================================
ARIMA(4, 1, 3)
MAPE: 15.29%
MAE: 56629.84
==================================================
ARIMA(4, 1, 4)
MAPE: 17.22%
MAE: 61650.57
==================================================

print(f"Best parameters model for SULAWESI BARAT:", best_param_sulawesi_barat)
print(f"with the lowest MAPE: {lowest_mape_sulawesi_barat.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_sulawesi_barat.round(2)}")

Best parameters model for SULAWESI BARAT: (0, 1, 3)
with the lowest MAPE: 9.41%
with the lowest MAE: 37205.0

plot_predictions_vs_actual(prediction_series=prediction_sulawesi_barat, actual_series=test_sulawesi_barat)

train_maluku = training_data["MALUKU"]
test_maluku = test_data["MALUKU"]

best_param_maluku, lowest_mape_maluku, lowest_mae_maluku, prediction_maluku = make_arima_prediction(train_maluku, test_maluku)

all_provinces_predictions['MALUKU'] = prediction_maluku

ARIMA(0, 0, 0)
MAPE: 47.25%
MAE: 77574.81
==================================================
ARIMA(0, 0, 1)
MAPE: 40.23%
MAE: 63707.94
==================================================
ARIMA(0, 0, 2)
MAPE: 35.8%
MAE: 57781.99
==================================================
ARIMA(0, 0, 3)
MAPE: 33.68%
MAE: 54727.08
==================================================
ARIMA(0, 0, 4)
MAPE: 32.36%
MAE: 51305.03
==================================================
ARIMA(0, 1, 0)
MAPE: 83.78%
MAE: 125734.52
==================================================
ARIMA(0, 1, 1)
MAPE: 29.82%
MAE: 42016.89
==================================================
ARIMA(0, 1, 2)
MAPE: 23.39%
MAE: 32397.84
==================================================
ARIMA(0, 1, 3)
MAPE: 19.67%
MAE: 27807.02
==================================================
ARIMA(0, 1, 4)
MAPE: 19.1%
MAE: 27472.22
==================================================
ARIMA(1, 0, 0)
MAPE: 32.73%
MAE: 49216.92
==================================================
ARIMA(1, 0, 1)
MAPE: 25.67%
MAE: 36588.9
==================================================
ARIMA(1, 0, 2)
MAPE: 25.99%
MAE: 37011.76
==================================================
ARIMA(1, 0, 3)
MAPE: 28.34%
MAE: 40486.23
==================================================
ARIMA(1, 0, 4)
MAPE: 25.86%
MAE: 35327.27
==================================================
ARIMA(1, 1, 0)
MAPE: 51.47%
MAE: 75150.44
==================================================
ARIMA(1, 1, 1)
MAPE: 20.76%
MAE: 29118.69
==================================================
ARIMA(1, 1, 2)
MAPE: 13.61%
MAE: 20473.33
==================================================
ARIMA(1, 1, 3)
MAPE: 20.05%
MAE: 28072.51
==================================================
ARIMA(1, 1, 4)
MAPE: 12.27%
MAE: 18910.57
==================================================
ARIMA(2, 0, 0)
MAPE: 28.28%
MAE: 40710.75
==================================================
ARIMA(2, 0, 1)
MAPE: 25.78%
MAE: 36734.33
==================================================
ARIMA(2, 0, 2)
MAPE: 26.73%
MAE: 38120.36
==================================================
ARIMA(2, 0, 3)
MAPE: 28.29%
MAE: 40380.2
==================================================
ARIMA(2, 0, 4)
MAPE: 30.41%
MAE: 43427.47
==================================================
ARIMA(2, 1, 0)
MAPE: 40.23%
MAE: 57925.21
==================================================
ARIMA(2, 1, 1)
MAPE: 13.51%
MAE: 21010.3
==================================================
ARIMA(2, 1, 2)
MAPE: 13.62%
MAE: 20562.85
==================================================
ARIMA(2, 1, 3)
MAPE: 11.85%
MAE: 18089.59
==================================================
ARIMA(2, 1, 4)
MAPE: 11.66%
MAE: 17817.8
==================================================
ARIMA(3, 0, 0)
MAPE: 26.91%
MAE: 38480.52
==================================================
ARIMA(3, 0, 1)
MAPE: 25.86%
MAE: 36865.45
==================================================
ARIMA(3, 0, 2)
MAPE: 26.06%
MAE: 37080.41
==================================================
ARIMA(3, 0, 3)
MAPE: 26.97%
MAE: 38358.88
==================================================
ARIMA(3, 0, 4)
MAPE: 28.72%
MAE: 38381.56
==================================================
ARIMA(3, 1, 0)
MAPE: 32.23%
MAE: 45801.09
==================================================
ARIMA(3, 1, 1)
MAPE: 14.04%
MAE: 21281.93
==================================================
ARIMA(3, 1, 2)
MAPE: 13.76%
MAE: 20706.28
==================================================
ARIMA(3, 1, 3)
MAPE: 12.06%
MAE: 18382.08
==================================================
ARIMA(3, 1, 4)
MAPE: 16.0%
MAE: 22997.06
==================================================
ARIMA(4, 0, 0)
MAPE: 24.99%
MAE: 35516.2
==================================================
ARIMA(4, 0, 1)
MAPE: 25.82%
MAE: 36759.43
==================================================
ARIMA(4, 0, 2)
MAPE: 29.14%
MAE: 42223.31
==================================================
ARIMA(4, 0, 3)
MAPE: 44.43%
MAE: 64964.09
==================================================
ARIMA(4, 0, 4)
MAPE: 28.26%
MAE: 38471.24
==================================================
ARIMA(4, 1, 0)
MAPE: 30.72%
MAE: 43453.57
==================================================
ARIMA(4, 1, 1)
MAPE: 13.98%
MAE: 20869.8
==================================================
ARIMA(4, 1, 2)
MAPE: 13.02%
MAE: 19768.96
==================================================
ARIMA(4, 1, 3)
MAPE: 11.91%
MAE: 18241.38
==================================================
ARIMA(4, 1, 4)
MAPE: 13.96%
MAE: 20178.47
==================================================

print(f"Best parameters model for MALUKU:", best_param_maluku)
print(f"with the lowest MAPE: {lowest_mape_maluku.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_maluku.round(2)}")

Best parameters model for MALUKU: (2, 1, 4)
with the lowest MAPE: 11.66%
with the lowest MAE: 17817.8

plot_predictions_vs_actual(prediction_series=prediction_maluku, actual_series=test_maluku)

train_maluku_utara = training_data["MALUKU UTARA"]
test_maluku_utara = test_data["MALUKU UTARA"]

best_param_maluku_utara, lowest_mape_maluku_utara, lowest_mae_maluku_utara, prediction_maluku_utara = make_arima_prediction(train_maluku_utara, test_maluku_utara)

all_provinces_predictions['MALUKU UTARA'] = prediction_maluku_utara

ARIMA(0, 0, 0)
MAPE: 38.11%
MAE: 67646.02
==================================================
ARIMA(0, 0, 1)
MAPE: 34.51%
MAE: 60795.82
==================================================
ARIMA(0, 0, 2)
MAPE: 41.41%
MAE: 74407.28
==================================================
ARIMA(0, 0, 3)
MAPE: 41.11%
MAE: 73692.16
==================================================
ARIMA(0, 0, 4)
MAPE: 30.72%
MAE: 53871.25
==================================================
ARIMA(0, 1, 0)
MAPE: 76.63%
MAE: 128671.89
==================================================
ARIMA(0, 1, 1)
MAPE: 63.86%
MAE: 106851.23
==================================================
ARIMA(0, 1, 2)
MAPE: 67.83%
MAE: 113521.89
==================================================
ARIMA(0, 1, 3)
MAPE: 46.2%
MAE: 77275.85
==================================================
ARIMA(0, 1, 4)
MAPE: 43.22%
MAE: 72282.28
==================================================
ARIMA(1, 0, 0)
MAPE: 25.97%
MAE: 44164.84
==================================================
ARIMA(1, 0, 1)
MAPE: 27.17%
MAE: 46544.8
==================================================
ARIMA(1, 0, 2)
MAPE: 25.32%
MAE: 42874.78
==================================================
ARIMA(1, 0, 3)
MAPE: 38.87%
MAE: 69132.21
==================================================
ARIMA(1, 0, 4)
MAPE: 31.41%
MAE: 53939.47
==================================================
ARIMA(1, 1, 0)
MAPE: 64.52%
MAE: 107929.56
==================================================
ARIMA(1, 1, 1)
MAPE: 67.87%
MAE: 113630.67
==================================================
ARIMA(1, 1, 2)
MAPE: 43.95%
MAE: 73795.08
==================================================
ARIMA(1, 1, 3)
MAPE: 43.93%
MAE: 73832.51
==================================================
ARIMA(1, 1, 4)
MAPE: 43.88%
MAE: 73697.68
==================================================
ARIMA(2, 0, 0)
MAPE: 26.68%
MAE: 45734.12
==================================================
ARIMA(2, 0, 1)
MAPE: 27.15%
MAE: 46479.94
==================================================
ARIMA(2, 0, 2)
MAPE: 25.92%
MAE: 44192.63
==================================================
ARIMA(2, 0, 3)
MAPE: 32.07%
MAE: 55580.08
==================================================
ARIMA(2, 0, 4)
MAPE: 33.11%
MAE: 56329.33
==================================================
ARIMA(2, 1, 0)
MAPE: 68.36%
MAE: 114384.54
==================================================
ARIMA(2, 1, 1)
MAPE: 69.23%
MAE: 115938.48
==================================================
ARIMA(2, 1, 2)
MAPE: 44.41%
MAE: 74504.01
==================================================
ARIMA(2, 1, 3)
MAPE: 42.49%
MAE: 70610.54
==================================================
ARIMA(2, 1, 4)
MAPE: 44.84%
MAE: 75161.35
==================================================
ARIMA(3, 0, 0)
MAPE: 26.8%
MAE: 45600.19
==================================================
ARIMA(3, 0, 1)
MAPE: 26.35%
MAE: 45140.53
==================================================
ARIMA(3, 0, 2)
MAPE: 23.49%
MAE: 41325.17
==================================================
ARIMA(3, 0, 3)
MAPE: 30.99%
MAE: 52283.58
==================================================
ARIMA(3, 0, 4)
MAPE: 29.38%
MAE: 52801.39
==================================================
ARIMA(3, 1, 0)
MAPE: 59.36%
MAE: 99450.14
==================================================
ARIMA(3, 1, 1)
MAPE: 43.41%
MAE: 73082.17
==================================================
ARIMA(3, 1, 2)
MAPE: 46.88%
MAE: 78493.22
==================================================
ARIMA(3, 1, 3)
MAPE: 35.28%
MAE: 58844.94
==================================================
ARIMA(3, 1, 4)
MAPE: 39.32%
MAE: 65346.12
==================================================
ARIMA(4, 0, 0)
MAPE: 28.76%
MAE: 49026.0
==================================================
ARIMA(4, 0, 1)
MAPE: 28.85%
MAE: 48928.91
==================================================
ARIMA(4, 0, 2)
MAPE: 28.03%
MAE: 47821.19
==================================================
ARIMA(4, 0, 3)
MAPE: 30.41%
MAE: 52838.32
==================================================
ARIMA(4, 0, 4)
MAPE: 30.95%
MAE: 53724.02
==================================================
ARIMA(4, 1, 0)
MAPE: 48.68%
MAE: 81342.9
==================================================
ARIMA(4, 1, 1)
MAPE: 42.28%
MAE: 71018.35
==================================================
ARIMA(4, 1, 2)
MAPE: 44.55%
MAE: 74583.38
==================================================
ARIMA(4, 1, 3)
MAPE: 57.9%
MAE: 97085.0
==================================================
ARIMA(4, 1, 4)
MAPE: 36.49%
MAE: 61013.01
==================================================

print(f"Best parameters model for MALUKU UTARA:", best_param_maluku_utara)
print(f"with the lowest MAPE: {lowest_mape_maluku_utara.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_maluku_utara.round(2)}")

Best parameters model for MALUKU UTARA: (3, 0, 2)
with the lowest MAPE: 23.49%
with the lowest MAE: 41325.17

plot_predictions_vs_actual(prediction_series=prediction_maluku_utara, actual_series=test_maluku_utara)

train_papua_barat = training_data["PAPUA BARAT"]
test_papua_barat = test_data["PAPUA BARAT"]

best_param_papua_barat, lowest_mape_papua_barat, lowest_mae_papua_barat, prediction_papua_barat = make_arima_prediction(train_papua_barat, test_papua_barat)

all_provinces_predictions['PAPUA BARAT'] = prediction_papua_barat

ARIMA(0, 0, 0)
MAPE: 101.18%
MAE: 33048.62
==================================================
ARIMA(0, 0, 1)
MAPE: 124.57%
MAE: 44232.69
==================================================
ARIMA(0, 0, 2)
MAPE: 164.76%
MAE: 60580.65
==================================================
ARIMA(0, 0, 3)
MAPE: 188.58%
MAE: 69398.8
==================================================
ARIMA(0, 0, 4)
MAPE: 240.44%
MAE: 88494.88
==================================================
ARIMA(0, 1, 0)
MAPE: 541.41%
MAE: 181002.48
==================================================
ARIMA(0, 1, 1)
MAPE: 393.21%
MAE: 131197.58
==================================================
ARIMA(0, 1, 2)
MAPE: 388.8%
MAE: 129784.16
==================================================
ARIMA(0, 1, 3)
MAPE: 368.51%
MAE: 124131.55
==================================================
ARIMA(0, 1, 4)
MAPE: 395.71%
MAE: 131162.86
==================================================
ARIMA(1, 0, 0)
MAPE: 353.07%
MAE: 121119.97
==================================================
ARIMA(1, 0, 1)
MAPE: 318.29%
MAE: 106836.5
==================================================
ARIMA(1, 0, 2)
MAPE: 321.04%
MAE: 107754.89
==================================================
ARIMA(1, 0, 3)
MAPE: 325.79%
MAE: 109968.98
==================================================
ARIMA(1, 0, 4)
MAPE: 305.84%
MAE: 98317.99
==================================================
ARIMA(1, 1, 0)
MAPE: 436.41%
MAE: 145369.43
==================================================
ARIMA(1, 1, 1)
MAPE: 384.56%
MAE: 128431.25
==================================================
ARIMA(1, 1, 2)
MAPE: 418.57%
MAE: 139500.98
==================================================
ARIMA(1, 1, 3)
MAPE: 373.11%
MAE: 125378.58
==================================================
ARIMA(1, 1, 4)
MAPE: 412.69%
MAE: 135810.49
==================================================
ARIMA(2, 0, 0)
MAPE: 335.51%
MAE: 112759.84
==================================================
ARIMA(2, 0, 1)
MAPE: 350.08%
MAE: 118260.21
==================================================
ARIMA(2, 0, 2)
MAPE: 336.85%
MAE: 113292.77
==================================================
ARIMA(2, 0, 3)
MAPE: 325.99%
MAE: 109934.08
==================================================
ARIMA(2, 0, 4)
MAPE: 343.4%
MAE: 110820.38
==================================================
ARIMA(2, 1, 0)
MAPE: 434.06%
MAE: 144590.09
==================================================
ARIMA(2, 1, 1)
MAPE: 383.85%
MAE: 128437.58
==================================================
ARIMA(2, 1, 2)
MAPE: 383.44%
MAE: 128511.04
==================================================
ARIMA(2, 1, 3)
MAPE: 416.53%
MAE: 139215.59
==================================================
ARIMA(2, 1, 4)
MAPE: 411.73%
MAE: 135758.49
==================================================
ARIMA(3, 0, 0)
MAPE: 340.02%
MAE: 114419.44
==================================================
ARIMA(3, 0, 1)
MAPE: 345.86%
MAE: 116630.55
==================================================
ARIMA(3, 0, 2)
MAPE: 327.46%
MAE: 110119.71
==================================================
ARIMA(3, 0, 3)
MAPE: 331.26%
MAE: 111578.77
==================================================
ARIMA(3, 0, 4)
MAPE: 342.75%
MAE: 110574.37
==================================================
ARIMA(3, 1, 0)
MAPE: 370.33%
MAE: 123901.36
==================================================
ARIMA(3, 1, 1)
MAPE: 366.58%
MAE: 122686.29
==================================================
ARIMA(3, 1, 2)
MAPE: 391.98%
MAE: 129156.87
==================================================
ARIMA(3, 1, 3)
MAPE: 421.31%
MAE: 138725.77
==================================================
ARIMA(3, 1, 4)
MAPE: 415.02%
MAE: 136604.1
==================================================
ARIMA(4, 0, 0)
MAPE: 306.16%
MAE: 102613.86
==================================================
ARIMA(4, 0, 1)
MAPE: 308.78%
MAE: 103517.02
==================================================
ARIMA(4, 0, 2)
MAPE: 312.36%
MAE: 104621.96
==================================================
ARIMA(4, 0, 3)
MAPE: 312.3%
MAE: 99754.52
==================================================
ARIMA(4, 0, 4)
MAPE: 336.06%
MAE: 106995.27
==================================================
ARIMA(4, 1, 0)
MAPE: 364.55%
MAE: 122024.51
==================================================
ARIMA(4, 1, 1)
MAPE: 364.15%
MAE: 122026.7
==================================================
ARIMA(4, 1, 2)
MAPE: 422.61%
MAE: 139101.3
==================================================
ARIMA(4, 1, 3)
MAPE: 399.99%
MAE: 131651.04
==================================================
ARIMA(4, 1, 4)
MAPE: 471.61%
MAE: 155063.98
==================================================

print(f"Best parameters model for PAPUA BARAT:", best_param_papua_barat)
print(f"with the lowest MAPE: {lowest_mape_papua_barat.round(2)}%") 
print(f"with the lowest MAE: {lowest_mae_papua_barat.round(2)}")

Best parameters model for PAPUA BARAT: (0, 0, 0)
with the lowest MAPE: 101.18%
with the lowest MAE: 33048.62

plot_predictions_vs_actual(prediction_series=prediction_papua_barat, actual_series=test_papua_barat)

train_papua = training_data["PAPUA"]
test_papua = test_data["PAPUA"]

best_param_papua, lowest_mape_papua, lowest_mae_papua, prediction_papua = make_arima_prediction(train_papua, test_papua)

all_provinces_predictions['PAPUA'] = prediction_papua

ARIMA(0, 0, 0)
MAPE: 20.81%
MAE: 19803.87
==================================================
ARIMA(0, 0, 1)
MAPE: 39.3%
MAE: 44344.21
==================================================
ARIMA(0, 0, 2)
MAPE: 62.89%
MAE: 69943.55
==================================================
ARIMA(0, 0, 3)
MAPE: 92.0%
MAE: 99983.51
==================================================
ARIMA(0, 0, 4)
MAPE: 120.57%
MAE: 130649.09
==================================================
ARIMA(0, 1, 0)
MAPE: 321.25%
MAE: 320065.63
==================================================
ARIMA(0, 1, 1)
MAPE: 228.83%
MAE: 227438.51
==================================================
ARIMA(0, 1, 2)
MAPE: 225.23%
MAE: 224002.86
==================================================
ARIMA(0, 1, 3)
MAPE: 220.75%
MAE: 219881.94
==================================================
ARIMA(0, 1, 4)
MAPE: 226.17%
MAE: 224548.33
==================================================
ARIMA(1, 0, 0)
MAPE: 212.29%
MAE: 215163.0
==================================================
ARIMA(1, 0, 1)
MAPE: 191.99%
MAE: 192295.54
==================================================
ARIMA(1, 0, 2)
MAPE: 193.5%
MAE: 193798.57
==================================================
ARIMA(1, 0, 3)
MAPE: 193.4%
MAE: 193708.84
==================================================
ARIMA(1, 0, 4)
MAPE: 192.91%
MAE: 191820.38
==================================================
ARIMA(1, 1, 0)
MAPE: 257.18%
MAE: 255226.39
==================================================
ARIMA(1, 1, 1)
MAPE: 224.3%
MAE: 223131.12
==================================================
ARIMA(1, 1, 2)
MAPE: 223.86%
MAE: 222733.99
==================================================
ARIMA(1, 1, 3)
MAPE: 220.57%
MAE: 219666.62
==================================================
ARIMA(1, 1, 4)
MAPE: 224.7%
MAE: 223423.94
==================================================
ARIMA(2, 0, 0)
MAPE: 198.79%
MAE: 199017.97
==================================================
ARIMA(2, 0, 1)
MAPE: 194.67%
MAE: 194890.29
==================================================
ARIMA(2, 0, 2)
MAPE: 193.34%
MAE: 193645.19
==================================================
ARIMA(2, 0, 3)
MAPE: 195.45%
MAE: 195717.0
==================================================
ARIMA(2, 0, 4)
MAPE: 161.26%
MAE: 162380.65
==================================================
ARIMA(2, 1, 0)
MAPE: 240.61%
MAE: 238925.34
==================================================
ARIMA(2, 1, 1)
MAPE: 223.53%
MAE: 222460.9
==================================================
ARIMA(2, 1, 2)
MAPE: 222.39%
MAE: 221399.37
==================================================
ARIMA(2, 1, 3)
MAPE: 219.21%
MAE: 217690.75
==================================================
ARIMA(2, 1, 4)
MAPE: 231.77%
MAE: 229664.84
==================================================
ARIMA(3, 0, 0)
MAPE: 195.52%
MAE: 195701.35
==================================================
ARIMA(3, 0, 1)
MAPE: 196.11%
MAE: 196322.34
==================================================
ARIMA(3, 0, 2)
MAPE: 193.62%
MAE: 193921.52
==================================================
ARIMA(3, 0, 3)
MAPE: 192.95%
MAE: 192646.88
==================================================
ARIMA(3, 0, 4)
MAPE: 161.49%
MAE: 162425.11
==================================================
ARIMA(3, 1, 0)
MAPE: 222.81%
MAE: 221628.07
==================================================
ARIMA(3, 1, 1)
MAPE: 220.27%
MAE: 219183.48
==================================================
ARIMA(3, 1, 2)
MAPE: 220.73%
MAE: 219625.77
==================================================
ARIMA(3, 1, 3)
MAPE: 223.18%
MAE: 221862.62
==================================================
ARIMA(3, 1, 4)
MAPE: 220.54%
MAE: 220259.21
==================================================
ARIMA(4, 0, 0)
MAPE: 194.93%
MAE: 195138.8
==================================================
ARIMA(4, 0, 1)
MAPE: 195.25%
MAE: 195471.04
==================================================
ARIMA(4, 0, 2)
MAPE: 199.09%
MAE: 199531.6
==================================================
ARIMA(4, 0, 3)
MAPE: 210.69%
MAE: 213087.3
==================================================
ARIMA(4, 0, 4)
MAPE: 176.61%
MAE: 177296.1
==================================================
ARIMA(4, 1, 0)
MAPE: 220.51%
MAE: 219400.31
==================================================
ARIMA(4, 1, 1)
MAPE: 220.04%
MAE: 218963.74
==================================================
ARIMA(4, 1, 2)
MAPE: 221.46%
MAE: 220373.48
==================================================
ARIMA(4, 1, 3)
MAPE: 221.07%
MAE: 220647.4
==================================================
ARIMA(4, 1, 4)
MAPE: 225.79%
MAE: 223271.66
==================================================

print(f"Best parameters model for PAPUA:", best_param_papua)
print(f"with the lowest MAPE: {lowest_mape_papua.round(2)}%")
print(f"with the lowest MAE: {lowest_mae_papua.round(2)}")

Best parameters model for PAPUA: (0, 0, 0)
with the lowest MAPE: 20.81%
with the lowest MAE: 19803.87

plot_predictions_vs_actual(prediction_series=prediction_papua, actual_series=test_papua)

all_provinces_predictions = all_provinces_predictions.round(0)

all_provinces_predictions.to_csv("all_provinces_predictions.csv")

prediction = pd.read_csv("all_provinces_predictions.csv")

prediction

prediction.set_index(test_data.index, inplace=True)

prediction = prediction.drop(columns=['Unnamed: 0'])

prediction

print(f"Number of provinces with MAPE <= 20%: {len(good_predictions)}")

for i, (province, mape) in enumerate(good_predictions.items(), start=1):
    print(f"{i}. {province}: {mape:.2f}%")

Number of provinces with MAPE <= 20%: 27
1. ACEH: 17.98%
2. SUMATERA UTARA: 8.83%
3. SUMATERA BARAT: 15.39%
4. RIAU: 12.32%
5. JAMBI: 9.94%
6. SUMATERA SELATAN: 8.02%
7. LAMPUNG: 8.43%
8. KEP. RIAU: 6.31%
9. DKI JAKARTA: 4.38%
10. JAWA BARAT: 10.68%
11. DI YOGYAKARTA: 17.35%
12. JAWA TIMUR: 9.12%
13. BANTEN: 7.39%
14. BALI: 2.37%
15. NUSA TENGGARA TIMUR: 4.56%
16. KALIMANTAN BARAT: 5.20%
17. KALIMANTAN TENGAH: 6.44%
18. KALIMANTAN SELATAN: 11.45%
19. KALIMANTAN TIMUR: 9.58%
20. KALIMANTAN UTARA: 6.67%
21. SULAWESI UTARA: 5.54%
22. SULAWESI TENGAH: 9.05%
23. SULAWESI SELATAN: 9.71%
24. SULAWESI TENGGARA: 6.82%
25. GORONTALO: 5.46%
26. SULAWESI BARAT: 9.41%
27. MALUKU: 11.66%

print(f"Percentage of provinces with MAPE <= 20%: {len(good_predictions) / len(provinces) * 100:.2f}%")

Percentage of provinces with MAPE <= 20%: 79.41%

!jupyter nbconvert --to html "./UAS2024_IF540L_Kelas GL_Kelompok_4.ipynb" --output-dir="./"

	0	1	2	3	4	5	6	7	8	9	...	30	31	32	33	34	35	36	37	38	39
0	NaN	ACEH	SUMATERA UTARA	SUMATERA BARAT	RIAU	JAMBI	SUMATERA SELATAN	BENGKULU	LAMPUNG	KEP. BANGKA BELITUNG	...	SULAWESI BARAT	MALUKU	MALUKU UTARA	PAPUA BARAT	PAPUA BARAT DAYA	PAPUA	PAPUA SELATAN	PAPUA TENGAH	PAPUA PEGUNUNGAN	INDONESIA
1	Januari	1387574	5094564	1667509	1990985	828879	1763349	414287	2082997	298322	...	197588	258094	125514	185287	-	313389	-	-	-	60784241
2	Februari	1111607	3282713	1313185	1425092	652908	1294947	304721	1612674	240226	...	134391	193290	76500	143094	-	209857	-	-	-	44588823
3	Maret	1113176	3442407	1420799	1541155	716028	1356470	336570	1606207	255054	...	163587	206894	166014	159903	-	224960	-	-	-	56669168
4	April	1155705	3676763	1573606	1563303	547378	1099872	287546	1164672	199678	...	174495	216036	190003	152087	-	209282	-	-	-	57856792
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2	Februari	937728	3392029	1758610	1449021	610850	1319045	308036	1338941	237943	...	354721	156854	174338	38474	75511	102856	17468	39547	35410	81107173
3	Maret	905565	3149185	1281044	1434953	612554	1285518	298351	1260766	224732	...	325813	161337	163305	35649	73040	103119	15993	37022	28300	78956390
4	April	1282532	3548654	1660760	1750903	886241	1854471	477429	1702675	297882	...	443291	190504	206819	36469	76064	107380	17842	32192	23991	104527211
5	Mei	995794	3419857	1302524	1488391	662793	1326810	339591	1508526	237959	...	342758	141499	161274	30025	68339	90245	13477	28351	23709	82909661
6	Juni	1065780	3369808	1642366	1446350	665827	1361245	358206	1658113	235572	...	344329	117608	143794	24109	60563	80858	10197	20750	20894	83472282

	ACEH	SUMATERA UTARA	SUMATERA BARAT	RIAU	JAMBI	SUMATERA SELATAN	BENGKULU	LAMPUNG	KEP. BANGKA BELITUNG	KEP. RIAU	...	SULAWESI BARAT	MALUKU	MALUKU UTARA	PAPUA BARAT	PAPUA BARAT DAYA	PAPUA	PAPUA SELATAN	PAPUA TENGAH	PAPUA PEGUNUNGAN	INDONESIA
2019-01-01	1387574	5094564	1667509	1990985	828879	1763349	414287	2082997	298322	434786	...	197588	258094	125514	185287	-	313389	-	-	-	60784241
2019-02-01	1111607	3282713	1313185	1425092	652908	1294947	304721	1612674	240226	351865	...	134391	193290	76500	143094	-	209857	-	-	-	44588823
2019-03-01	1113176	3442407	1420799	1541155	716028	1356470	336570	1606207	255054	358547	...	163587	206894	166014	159903	-	224960	-	-	-	56669168
2019-04-01	1155705	3676763	1573606	1563303	547378	1099872	287546	1164672	199678	360392	...	174495	216036	190003	152087	-	209282	-	-	-	57856792
2019-05-01	1070909	3469100	1511963	1574681	438508	917156	258241	877643	122301	358416	...	152740	228658	163855	161711	-	235428	-	-	-	51548736
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2024-02-01	937728	3392029	1758610	1449021	610850	1319045	308036	1338941	237943	290290	...	354721	156854	174338	38474	75511	102856	17468	39547	35410	81107173
2024-03-01	905565	3149185	1281044	1434953	612554	1285518	298351	1260766	224732	268990	...	325813	161337	163305	35649	73040	103119	15993	37022	28300	78956390
2024-04-01	1282532	3548654	1660760	1750903	886241	1854471	477429	1702675	297882	333667	...	443291	190504	206819	36469	76064	107380	17842	32192	23991	104527211
2024-05-01	995794	3419857	1302524	1488391	662793	1326810	339591	1508526	237959	276392	...	342758	141499	161274	30025	68339	90245	13477	28351	23709	82909661
2024-06-01	1065780	3369808	1642366	1446350	665827	1361245	358206	1658113	235572	241548	...	344329	117608	143794	24109	60563	80858	10197	20750	20894	83472282

	ACEH	SUMATERA UTARA	SUMATERA BARAT	RIAU	JAMBI	SUMATERA SELATAN	BENGKULU	LAMPUNG	KEP. BANGKA BELITUNG	KEP. RIAU	...	SULAWESI UTARA	SULAWESI TENGAH	SULAWESI SELATAN	SULAWESI TENGGARA	GORONTALO	SULAWESI BARAT	MALUKU	MALUKU UTARA	PAPUA BARAT	PAPUA
time_series
2019-01-01	1387574	5094564	1667509	1990985	828879	1763349	414287	2082997	298322	434786	...	256496	451254	2054894	534762	90789	197588	258094	125514	185287	313389
2019-02-01	1111607	3282713	1313185	1425092	652908	1294947	304721	1612674	240226	351865	...	112937	308973	1402775	405350	48019	134391	193290	76500	143094	209857
2019-03-01	1113176	3442407	1420799	1541155	716028	1356470	336570	1606207	255054	358547	...	197795	374463	1654575	436423	84467	163587	206894	166014	159903	224960
2019-04-01	1155705	3676763	1573606	1563303	547378	1099872	287546	1164672	199678	360392	...	221246	374029	1799377	487941	102283	174495	216036	190003	152087	209282
2019-05-01	1070909	3469100	1511963	1574681	438508	917156	258241	877643	122301	358416	...	235233	368161	1653101	445093	109827	152740	228658	163855	161711	235428
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2024-02-01	937728	3392029	1758610	1449021	610850	1319045	308036	1338941	237943	290290	...	628713	831294	3016298	1145554	270873	354721	156854	174338	38474	102856
2024-03-01	905565	3149185	1281044	1434953	612554	1285518	298351	1260766	224732	268990	...	625920	809385	2891023	1039727	274795	325813	161337	163305	35649	103119
2024-04-01	1282532	3548654	1660760	1750903	886241	1854471	477429	1702675	297882	333667	...	762190	1061864	3551378	1442912	376567	443291	190504	206819	36469	107380
2024-05-01	995794	3419857	1302524	1488391	662793	1326810	339591	1508526	237959	276392	...	668197	814471	3087984	1070042	285885	342758	141499	161274	30025	90245
2024-06-01	1065780	3369808	1642366	1446350	665827	1361245	358206	1658113	235572	241548	...	671549	774521	3145350	1110037	287451	344329	117608	143794	24109	80858

	ACEH	SUMATERA UTARA	SUMATERA BARAT	RIAU	JAMBI	SUMATERA SELATAN	BENGKULU	LAMPUNG	KEP. BANGKA BELITUNG	KEP. RIAU	...	SULAWESI UTARA	SULAWESI TENGAH	SULAWESI SELATAN	SULAWESI TENGGARA	GORONTALO	SULAWESI BARAT	MALUKU	MALUKU UTARA	PAPUA BARAT	PAPUA
time_series
2019-01-01	1387574	5094564	1667509	1990985	828879	1763349	414287	2082997	298322	434786	...	256496	451254	2054894	534762	90789	197588	258094	125514	185287	313389
2019-02-01	1111607	3282713	1313185	1425092	652908	1294947	304721	1612674	240226	351865	...	112937	308973	1402775	405350	48019	134391	193290	76500	143094	209857
2019-03-01	1113176	3442407	1420799	1541155	716028	1356470	336570	1606207	255054	358547	...	197795	374463	1654575	436423	84467	163587	206894	166014	159903	224960
2019-04-01	1155705	3676763	1573606	1563303	547378	1099872	287546	1164672	199678	360392	...	221246	374029	1799377	487941	102283	174495	216036	190003	152087	209282
2019-05-01	1070909	3469100	1511963	1574681	438508	917156	258241	877643	122301	358416	...	235233	368161	1653101	445093	109827	152740	228658	163855	161711	235428
2019-06-01	1800152	5774370	2910574	2179811	572785	1380350	407898	1653991	178462	460205	...	325311	380756	2395339	621228	163192	221961	224863	119683	172118	240501
2019-07-01	1323272	3996409	1719018	1646721	498688	848202	292552	992730	148190	413445	...	336575	323650	1917506	487514	123297	173239	111095	102619	164784	234623
2019-08-01	1508073	3699782	1207708	1227426	620967	1265112	353997	1422192	182592	286867	...	305167	506559	2516211	532628	131352	262037	104002	99582	158220	224909
2019-09-01	1174513	3208776	1011691	1018291	580974	1121514	347174	1191629	171412	244300	...	267290	404438	2106120	439248	106151	204889	91767	85337	73498	174820
2019-10-01	1219323	3419415	1094813	1131194	533651	1276480	342352	1269517	195073	274522	...	307207	469816	2341121	303878	139035	242300	96055	177456	93397	204906
2019-11-01	1198958	3416228	1078273	1119316	526013	1168308	340572	1276815	198154	287732	...	286146	494853	2345045	300770	120291	236861	97869	163370	94620	212794
2019-12-01	1597524	5440703	2292513	2087900	702943	1575018	412683	1871463	239683	448736	...	293133	418240	2230009	580664	127908	210792	243228	124114	180580	280181
2020-01-01	483897	2012511	863282	619363	264201	620543	155102	876254	127282	138085	...	156614	185796	1027653	206468	48443	105991	38562	46810	46741	75045
2020-02-01	430589	1466193	779785	548129	250082	584230	140647	782363	107273	112726	...	154197	176960	932636	193500	44850	97671	36768	47758	44158	73078
2020-03-01	351710	1109440	623248	464347	206088	483516	117920	658446	83654	68659	...	158677	145200	759362	143516	54723	69873	29688	38527	34282	57571
2020-04-01	157764	442072	226466	215636	114969	234250	61421	356647	34757	19843	...	66627	65380	287133	71545	33603	26873	10479	22439	9348	24783
2020-05-01	258310	576278	250931	249411	133020	223800	67769	395199	48428	17661	...	58036	65852	308242	71224	27476	22014	7978	12649	11657	26829
2020-06-01	298482	864667	562966	390121	183620	339725	100775	559053	53351	29392	...	112243	86803	547513	106867	49003	49222	11914	23881	17414	35260
2020-07-01	333571	1082687	754303	440119	235975	486690	131746	779034	70022	47791	...	193492	119534	663996	146936	68769	61561	15625	36540	22301	45320
2020-08-01	392055	1291700	827245	497048	258405	555501	154764	880796	87978	46331	...	259030	157123	909748	181890	83192	80440	21065	43536	23616	46725
2020-09-01	293602	1016798	555478	422867	228973	484199	127706	686462	73888	48314	...	282281	132882	850322	206855	94892	75891	23965	47792	23906	50583
2020-10-01	340415	1232110	672099	520924	240877	557537	146378	788792	81326	60644	...	335445	109312	959171	193265	108500	75517	29428	50275	26119	55847
2020-11-01	322558	1120288	661261	463144	242326	514748	131395	737712	86222	61440	...	326566	137047	906014	191705	101882	75153	30269	48505	32062	58583
2020-12-01	479226	1831624	1115758	674613	326026	745355	184734	1024964	111916	94695	...	377599	189733	1063264	269241	132725	110693	147307	83999	50886	136898
2021-01-01	396953	1424081	775782	507998	247256	541878	139180	707904	65347	60617	...	252298	140380	743342	209867	90759	124447	78120	68567	56475	124573
2021-02-01	421243	1375473	735587	482533	225630	511557	131185	647312	72930	64867	...	284672	139294	724546	189183	92165	77882	47482	47555	49044	56995
2021-03-01	445804	1415803	793796	511576	239867	553847	137722	746409	87341	75329	...	316834	177093	847765	208125	103269	81571	48787	54200	41249	69170
2021-04-01	378402	1369304	712644	451372	226476	516146	124531	706069	84283	70355	...	319841	154719	765199	187883	89741	70523	71019	51844	35284	86207
2021-05-01	599117	1674445	1025889	501143	280176	608248	179928	975559	93531	50771	...	372363	187679	937945	237647	117211	88146	50091	57892	31565	56054
2021-06-01	457822	1571487	894952	521519	255590	574663	159098	791886	93222	65954	...	344017	183102	928134	212043	100327	91217	55585	57866	42558	86681
2021-07-01	445355	1127318	664848	486997	240188	499604	126188	564563	73819	36721	...	237853	155542	838925	190375	92090	89158	32749	44031	26814	50949
2021-08-01	397120	1139616	635405	418261	205004	477028	123317	602800	62355	40978	...	237457	131074	723167	179936	85478	80028	38200	46619	29913	48378
2021-09-01	440652	1329028	717460	480952	234360	554767	139828	749160	80206	73747	...	290810	157759	824543	214553	97555	90870	46237	51543	42337	90643
2021-10-01	490983	1641459	829981	542388	277376	624377	160489	835530	109459	88509	...	340274	187825	960245	237909	104446	91955	52031	67386	40861	79326
2021-11-01	445046	1433761	733146	503440	247704	568239	138376	760453	101097	85577	...	333070	168918	842440	220257	93069	86912	51355	51408	33842	63345
2021-12-01	615908	2256408	1084422	735090	367540	796983	210851	1089221	140399	122247	...	403468	243526	1091834	293377	122720	109793	68109	73794	55063	114802
2022-01-01	526417	2022964	926043	636543	302504	650971	180768	878496	122602	93349	...	327133	195589	930906	243982	104369	86174	41334	60617	35776	71001
2022-02-01	501651	1636066	814741	581783	255285	538004	137886	694763	99338	75268	...	260815	173663	803391	220654	88530	83763	35725	55228	30984	63721
2022-03-01	585675	1846809	1007630	673534	313009	691513	172874	866807	142414	105778	...	347157	609507	3267252	1495127	151616	425016	49734	141231	37297	76243
2022-04-01	434633	1658463	838560	597000	310853	725485	160737	934561	125002	104207	...	349618	691868	3768595	1585129	140420	516032	57885	152337	34020	69361
2022-05-01	935025	2896949	1655583	897026	449078	970391	293433	1412731	183098	138874	...	503507	817447	4619651	1592211	189314	611768	56722	166862	38551	77379
2022-06-01	566361	1987624	988346	692293	316554	679215	173024	866263	135070	147934	...	394131	597158	2492319	1050720	130595	409575	51382	140421	39956	81989
2022-07-01	748573	2216932	1176922	795698	364514	834754	203448	1043996	146618	149456	...	436556	646673	2732814	1231851	140784	442861	48932	157797	39706	86039
2022-08-01	531072	1678953	905311	662795	285681	659155	158883	772720	117743	123680	...	352431	532060	2136650	1097809	113456	373165	46979	135693	40948	84147
2022-09-01	500673	1664991	891560	677309	280278	668784	159439	833337	120832	123914	...	360004	515189	1803376	1044426	109350	318147	52568	132686	39159	78700
2022-10-01	513056	1785747	949081	692329	289153	665454	154957	834108	126635	132432	...	365860	543272	1922182	1056378	100365	311722	52357	119219	40627	81068
2022-11-01	471520	1525633	877671	640199	262087	603787	133058	762778	115001	131230	...	322748	555548	1834305	1136194	92496	310736	43671	115762	36457	78388
2022-12-01	639922	2283325	1307815	857727	351987	804851	185142	1025144	144054	185232	...	401895	575043	2287771	1028565	117518	345542	75278	133388	56395	111840
2023-01-01	663437	2962641	1345098	914340	380949	870450	211866	1149078	164006	176025	...	421675	667588	2682502	1372219	139879	423949	66046	118409	47444	94530
2023-02-01	517850	1870691	1024017	729078	293566	714967	151670	900023	124730	153373	...	329629	567749	2225280	1299527	114908	380529	49253	109334	38921	76336
2023-03-01	557272	1852752	1056957	759760	302700	703181	154106	877374	139812	162989	...	358498	695711	2455952	1389727	132863	425711	55659	136684	40975	87194
2023-04-01	756915	2673955	1373374	935938	414742	1010231	248057	1429885	185529	162775	...	468296	702310	2503880	1281173	164216	399963	62401	142086	39759	87917
2023-05-01	791968	2556839	1538507	977332	421741	1021583	241660	1376686	209111	200190	...	460407	716311	2472401	1294740	162712	406264	75390	148300	49782	100494
2023-06-01	701963	2416258	1407632	1016640	424987	968252	225886	1208281	218299	230629	...	480374	465410	1983399	899696	154749	297134	85195	162908	62022	133566
2023-07-01	871519	2462212	1413899	1048542	433896	1013893	241961	1293197	234099	211471	...	492400	377459	2212555	838914	156871	294755	77652	157597	58569	123085
2023-08-01	618671	1889717	1072952	893939	375376	867202	199228	997673	206938	180206	...	422694	302353	1397173	480612	134793	157794	78196	144689	53329	114107
2023-09-01	565184	1847654	1083013	866457	368132	867562	204566	1030417	188694	179288	...	440086	251645	1188982	376071	139730	110449	69657	135130	46999	107044
2023-10-01	588020	1958158	1121805	908253	386128	858520	209482	1058924	173431	186323	...	427074	264313	1177955	365686	131858	111448	79054	126786	53875	119299
2023-11-01	637519	1870337	904282	762348	371185	725317	187859	894880	137343	166685	...	390052	284303	1233382	386244	125945	126041	78630	123916	57701	125457
2023-12-01	1673468	3251911	1350709	1334426	609785	1373335	680801	1544279	400977	285542	...	707661	993915	3893499	1085338	347397	376924	284711	302181	213062	419322

	ACEH	SUMATERA UTARA	SUMATERA BARAT	RIAU	JAMBI	SUMATERA SELATAN	BENGKULU	LAMPUNG	KEP. BANGKA BELITUNG	KEP. RIAU	...	SULAWESI UTARA	SULAWESI TENGAH	SULAWESI SELATAN	SULAWESI TENGGARA	GORONTALO	SULAWESI BARAT	MALUKU	MALUKU UTARA	PAPUA BARAT	PAPUA
time_series
2024-01-01	1009785	4055407	1767621	1546102	665652	1425715	361373	1618490	250498	291092	...	700470	941412	3390270	1200314	284274	396450	190408	190002	44841	129823
2024-02-01	937728	3392029	1758610	1449021	610850	1319045	308036	1338941	237943	290290	...	628713	831294	3016298	1145554	270873	354721	156854	174338	38474	102856
2024-03-01	905565	3149185	1281044	1434953	612554	1285518	298351	1260766	224732	268990	...	625920	809385	2891023	1039727	274795	325813	161337	163305	35649	103119
2024-04-01	1282532	3548654	1660760	1750903	886241	1854471	477429	1702675	297882	333667	...	762190	1061864	3551378	1442912	376567	443291	190504	206819	36469	107380
2024-05-01	995794	3419857	1302524	1488391	662793	1326810	339591	1508526	237959	276392	...	668197	814471	3087984	1070042	285885	342758	141499	161274	30025	90245
2024-06-01	1065780	3369808	1642366	1446350	665827	1361245	358206	1658113	235572	241548	...	671549	774521	3145350	1110037	287451	344329	117608	143794	24109	80858

	count	mean	std	min	25%	50%	75%	max
ACEH	66.0	718680.58	382615.41	157764.0	445123.25	576018.0	981277.50	1800152.0
SUMATERA UTARA	66.0	2295125.79	1142919.54	442072.0	1441869.00	1880204.0	3241127.25	5774370.0
SUMATERA BARAT	66.0	1102163.92	450389.00	226466.0	799032.25	1024953.0	1349306.25	2910574.0
RIAU	66.0	891240.53	468403.57	215636.0	513913.00	732084.0	1128224.50	2179811.0
JAMBI	66.0	388174.82	176172.31	114969.0	251382.75	321290.0	519181.75	886241.0
SUMATERA SELATAN	66.0	845210.15	364133.13	223800.0	560212.50	725401.0	1080299.75	1854471.0
BENGKULU	66.0	221263.94	110636.98	61421.0	140032.75	182751.0	293212.75	680801.0
LAMPUNG	66.0	1037864.09	367953.26	356647.0	774298.50	917292.0	1274990.50	2082997.0
KEP. BANGKA BELITUNG	66.0	150078.71	71315.41	34757.0	93299.25	136206.5	197383.75	400977.0
KEP. RIAU	66.0	172271.14	117132.36	17661.0	74127.25	143404.0	262817.50	460205.0
DKI JAKARTA	66.0	4433348.74	1552839.20	800210.0	3491046.75	4554059.5	5544983.00	7514043.0
JAWA BARAT	66.0	10613106.47	3110295.40	2572442.0	8589925.00	10942225.0	12650204.00	17758640.0
JAWA TENGAH	66.0	10193064.50	3010379.59	3635334.0	8138358.50	10276045.5	11654916.75	17809415.0
DI YOGYAKARTA	66.0	2108625.33	724740.63	665050.0	1681075.50	2068626.5	2569083.25	4267235.0
JAWA TIMUR	66.0	13596095.94	5006523.07	3397447.0	9816701.50	12883755.5	17434262.75	27190607.0
BANTEN	66.0	3253867.20	776173.10	1058760.0	2693869.25	3323129.0	3904923.25	4804045.0
BALI	66.0	1266224.14	483079.22	261406.0	883655.50	1252137.0	1668700.25	2090327.0
NUSA TENGGARA BARAT	66.0	585661.68	391182.33	114758.0	287080.50	401643.0	772498.25	1718321.0
NUSA TENGGARA TIMUR	66.0	385251.06	199238.15	78698.0	233290.50	316738.0	590989.75	800071.0
KALIMANTAN BARAT	66.0	382871.83	238703.12	58284.0	193327.50	301044.5	627326.25	965873.0
KALIMANTAN TENGAH	66.0	300917.70	166820.62	75118.0	178419.50	236010.5	458173.00	867859.0
KALIMANTAN SELATAN	66.0	554253.20	269398.74	140584.0	379606.25	475192.0	685249.75	1781498.0
KALIMANTAN TIMUR	66.0	514911.64	297020.13	91121.0	218684.25	527827.5	767912.50	1174998.0
KALIMANTAN UTARA	66.0	53940.70	40851.64	8536.0	25372.00	33838.0	76940.00	144538.0
SULAWESI UTARA	66.0	352061.21	150507.57	58036.0	262433.75	334257.5	417123.25	762190.0
SULAWESI TENGAH	66.0	400738.73	259692.87	65380.0	174487.25	371095.0	573219.50	1061864.0
SULAWESI SELATAN	66.0	1772237.68	990506.82	287133.0	914344.50	1726976.0	2440798.75	4619651.0
SULAWESI TENGGARA	66.0	621230.98	470552.20	71224.0	210411.00	437835.5	1066626.00	1592211.0
GORONTALO	66.0	129516.33	69744.69	27476.0	92247.75	114182.0	139841.75	376567.0
SULAWESI BARAT	66.0	216077.35	144775.77	22014.0	88399.00	168413.0	343936.25	611768.0
MALUKU	66.0	87080.08	67570.06	7978.0	46422.50	60143.0	102468.75	284711.0
MALUKU UTARA	66.0	108003.88	56533.29	12649.0	52433.00	118814.0	147397.25	302181.0
PAPUA BARAT	66.0	60737.50	48973.59	9348.0	34532.50	40961.5	56455.00	213062.0
PAPUA	66.0	114269.74	74771.29	24783.0	69771.00	86937.5	125236.00	419322.0

	Unnamed: 0	ACEH	SUMATERA UTARA	SUMATERA BARAT	RIAU	JAMBI	SUMATERA SELATAN	BENGKULU	LAMPUNG	KEP. BANGKA BELITUNG	...	SULAWESI UTARA	SULAWESI TENGAH	SULAWESI SELATAN	SULAWESI TENGGARA	GORONTALO	SULAWESI BARAT	MALUKU	MALUKU UTARA	PAPUA BARAT	PAPUA
0	2024-01-01	1283482.0	3103771.0	1350714.0	1317270.0	574645.0	1360872.0	475724.0	1363951.0	324168.0	...	620987.0	952641.0	3328107.0	1078504.0	285826.0	370192.0	197014.0	276173.0	67711.0	118892.0
1	2024-02-01	1187839.0	3341895.0	1350675.0	1373413.0	575500.0	1390412.0	419074.0	1294200.0	305676.0	...	629553.0	1014809.0	3270813.0	1133313.0	281811.0	409269.0	188388.0	230579.0	70069.0	121876.0
2	2024-03-01	1156738.0	3345807.0	1350679.0	1364115.0	599613.0	1392651.0	436341.0	1254520.0	292529.0	...	633828.0	814419.0	2984944.0	1090162.0	284698.0	335596.0	170975.0	220091.0	69501.0	120095.0
3	2024-04-01	1181740.0	3166725.0	1350717.0	1310724.0	651737.0	1334315.0	395557.0	1522195.0	282141.0	...	650086.0	838803.0	2920306.0	1123996.0	289740.0	334255.0	163139.0	187807.0	69837.0	119977.0
4	2024-05-01	1155904.0	3192265.0	1350737.0	1298180.0	635986.0	1332158.0	388219.0	1473739.0	276638.0	...	648918.0	767498.0	2750167.0	1096223.0	289498.0	334583.0	148149.0	178588.0	67064.0	117913.0
5	2024-06-01	1091959.0	3222922.0	1350721.0	1286268.0	615376.0	1342291.0	364871.0	1358111.0	273983.0	...	651874.0	746346.0	2641086.0	1113581.0	288218.0	328899.0	142722.0	156221.0	63677.0	112488.0

UJIAN AKHIR SEMESTER IF540-L MACHINE LEARNING¶

Semester Gasal 2024/2025¶

FORECASTING TRENDS OF DOMESTIC TOURIST VISITS USING THE ARIMA METHOD¶

Instruction¶

Caution¶

Group - 4¶

Members :¶

The dataset that are being used for the project¶

Dataset Description:¶

Please describe the dataset with much detail if possible¶

Project Showcase¶

Import libraries¶

Data preprocessing¶

Dataset 2019¶

Dataset 2020¶

Dataset 2021¶

Dataset 2022¶

Dataset 2023¶

Dataset 2024¶

Merging the 2019-2024 dataset¶

Drop unnecessary features¶

Combine/concat the datasets (2019-2024) and transpose the dataset¶

Create new index and set feature names after concatenating datasets¶

Handling missing values¶

Convert data to numeric¶

Drop unnecessary feature¶

Create new dataset¶

Split data to training and test¶

Check data stationarity¶

Function to check stationarity using ACF plot¶

Make ACF plot for each province¶

Function to check stationarity using PACF Plot¶

Make PACF plot for each province¶

Function to check stationarity using Augmented Dickey-Fuller¶

Check stationarity for each province using Augmented Dickey-Fuller¶

List stationary and non-stationary provinces¶

Descriptive statistic¶

Calculating descriptive statistics¶

Mean of domestic tourist visits per province¶

Median of domestic tourist visits per province¶

Standard deviation of domestic tourist visits per province¶

Min and max of domestic tourist visits per province¶

Build model¶

Initialize variables¶

Function to implement model validation using grid search¶

Function to make prediction vs actual plot¶

ACEH (1)¶

Split ACEH dataset¶

Make prediction for ACEH¶

Best Parameter, lowest MAPE, and lowest MAE for ACEH¶

Make prediction vs actual plot for ACEH¶

SUMATERA UTARA (2)¶

Split SUMATERA UTARA dataset¶

Make prediction for SUMATERA UTARA¶

Best parameter, lowest MAPE, and lowest MAE for SUMATERA UTARA¶

Make prediction vs actual plot for SUMATERA UTARA¶

SUMATERA BARAT (3)¶

Split SUMATERA BARAT dataset¶

Make prediction for SUMATERA BARAT¶

Best parameter, lowest MAPE, and lowest MAE for SUMATERA BARAT¶

Make prediction vs actual plot for SUMATERA BARAT¶

RIAU (4)¶

Split RIAU dataset¶

Make prediction for RIAU¶

Best parameter, lowest MAPE, and lowest MAE for RIAU¶

Make prediction vs actual plot for RIAU¶

JAMBI (5)¶

Split JAMBI dataset¶

Make prediction for JAMBI¶

Best parameter, lowest MAPE, and lowest MAE for JAMBI¶

Make prediction vs actual plot for JAMBI¶

SUMATERA SELATAN (6)¶

Split SUMATERA SELATAN dataset¶

Make prediction for SUMATERA SELATAN¶

Best parameter, lowest MAPE, and lowest MAE for SUMATERA SELATAN¶

Make prediction vs actual plot for SUMATERA SELATAN¶

BENGKULU (7)¶

Split BENGKULU dataset¶

Make prediction for BENGKULU¶

Best parameter, lowest MAPE, and lowest MAE for BENGKULU¶

UJIAN AKHIR SEMESTER IF540-L MACHINE LEARNING
¶

Semester Gasal 2024/2025
¶

FORECASTING TRENDS OF DOMESTIC TOURIST VISITS USING THE ARIMA METHOD
¶